Kernel bandwidth optimization in spike rate estimation.
Shimazaki, Hideaki; Shinomoto, Shigeru
2010-08-01
Kernel smoother and a time-histogram are classical tools for estimating an instantaneous rate of spike occurrences. We recently established a method for selecting the bin width of the time-histogram, based on the principle of minimizing the mean integrated square error (MISE) between the estimated rate and unknown underlying rate. Here we apply the same optimization principle to the kernel density estimation in selecting the width or "bandwidth" of the kernel, and further extend the algorithm to allow a variable bandwidth, in conformity with data. The variable kernel has the potential to accurately grasp non-stationary phenomena, such as abrupt changes in the firing rate, which we often encounter in neuroscience. In order to avoid possible overfitting that may take place due to excessive freedom, we introduced a stiffness constant for bandwidth variability. Our method automatically adjusts the stiffness constant, thereby adapting to the entire set of spike data. It is revealed that the classical kernel smoother may exhibit goodness-of-fit comparable to, or even better than, that of modern sophisticated rate estimation methods, provided that the bandwidth is selected properly for a given set of spike data, according to the optimization methods presented here.
A continuous entropy rate estimator for spike trains using a K-means-based context tree.
Lin, Tiger W; Reeke, George N
2010-04-01
Entropy rate quantifies the change of information of a stochastic process (Cover & Thomas, 2006). For decades, the temporal dynamics of spike trains generated by neurons has been studied as a stochastic process (Barbieri, Quirk, Frank, Wilson, & Brown, 2001; Brown, Frank, Tang, Quirk, & Wilson, 1998; Kass & Ventura, 2001; Metzner, Koch, Wessel, & Gabbiani, 1998; Zhang, Ginzburg, McNaughton, & Sejnowski, 1998). We propose here to estimate the entropy rate of a spike train from an inhomogeneous hidden Markov model of the spike intervals. The model is constructed by building a context tree structure to lay out the conditional probabilities of various subsequences of the spike train. For each state in the Markov chain, we assume a gamma distribution over the spike intervals, although any appropriate distribution may be employed as circumstances dictate. The entropy and confidence intervals for the entropy are calculated from bootstrapping samples taken from a large raw data sequence. The estimator was first tested on synthetic data generated by multiple-order Markov chains, and it always converged to the theoretical Shannon entropy rate (except in the case of a sixth-order model, where the calculations were terminated before convergence was reached). We also applied the method to experimental data and compare its performance with that of several other methods of entropy estimation.
Effects of phase on homeostatic spike rates.
Fisher, Nicholas; Talathi, Sachin S; Carney, Paul R; Ditto, William L
2010-05-01
Recent experimental results by Talathi et al. (Neurosci Lett 455:145-149, 2009) showed a divergence in the spike rates of two types of population spike events, representing the putative activity of the excitatory and inhibitory neurons in the CA1 area of an animal model for temporal lobe epilepsy. The divergence in the spike rate was accompanied by a shift in the phase of oscillations between these spike rates leading to a spontaneous epileptic seizure. In this study, we propose a model of homeostatic synaptic plasticity which assumes that the target spike rate of populations of excitatory and inhibitory neurons in the brain is a function of the phase difference between the excitatory and inhibitory spike rates. With this model of homeostatic synaptic plasticity, we are able to simulate the spike rate dynamics seen experimentally by Talathi et al. in a large network of interacting excitatory and inhibitory neurons using two different spiking neuron models. A drift analysis of the spike rates resulting from the homeostatic synaptic plasticity update rule allowed us to determine the type of synapse that may be primarily involved in the spike rate imbalance in the experimental observation by Talathi et al. We find excitatory neurons, particularly those in which the excitatory neuron is presynaptic, have the most influence in producing the diverging spike rates and causing the spike rates to be anti-phase. Our analysis suggests that the excitatory neuronal population, more specifically the excitatory to excitatory synaptic connections, could be implicated in a methodology designed to control epileptic seizures.
Parameter Estimation of a Spiking Silicon Neuron
Russell, Alexander; Mazurek, Kevin; Mihalaş, Stefan; Niebur, Ernst; Etienne-Cummings, Ralph
2012-01-01
Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. Parameter estimation of a single neuron model, such that the model’s output matches that of a biological neuron is an extremely important task. Hand tuning of parameters to obtain such behaviors is a difficult and time consuming process. This is further complicated when the neuron is instantiated in silicon (an attractive medium in which to implement these models) as fabrication imperfections make the task of parameter configuration more complex. In this paper we show two methods to automate the configuration of a silicon (hardware) neuron’s parameters. First, we show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neuron’s output to desired spike times. We then show how a distance based method which approximates the negative log likelihood of the lognormal distribution can also be used to tune the neuron’s parameters. We conclude that the distance based method is better suited for parameter configuration of silicon neurons due to its superior optimization speed. PMID:23852978
Correcting the bias of spike field coherence estimators due to a finite number of spikes.
Grasse, D W; Moxon, K A
2010-07-01
The coherence between oscillatory activity in local field potentials (LFPs) and single neuron action potentials, or spikes, has been suggested as a neural substrate for the representation of information. The power spectrum of a spike-triggered average (STA) is commonly used to estimate spike field coherence (SFC). However, when a finite number of spikes is used to construct the STA, the coherence estimator is biased. We introduce here a correction for the bias imposed by the limited number of spikes available in experimental conditions. In addition, we present an alternative method for estimating SFC from an STA by using a filter bank approach. This method is shown to be more appropriate in some analyses, such as comparing coherence across frequency bands. The proposed bias correction is a linear transformation derived from an idealized model of spike-field interaction but is shown to hold in more realistic settings. Uncorrected and corrected SFC estimates from both estimation methods are compared across multiple simulated spike-field models and experimentally collected data. The bias correction was shown to reduce the bias of the estimators, but add variance. However, the corrected estimates had a reduced or unchanged mean squared error in the majority of conditions evaluated. The bias correction provides an effective way to reduce bias in an SFC estimator without increasing the mean squared error.
Asynchronous Rate Chaos in Spiking Neuronal Circuits
Harish, Omri; Hansel, David
2015-01-01
The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results. PMID:26230679
Error estimation for reconstruction of neuronal spike firing from fast calcium imaging.
Liu, Xiuli; Lv, Xiaohua; Quan, Tingwei; Zeng, Shaoqun
2015-02-01
Calcium imaging is becoming an increasingly popular technology to indirectly measure activity patterns in local neuronal networks. Calcium transients reflect neuronal spike patterns allowing for spike train reconstructed from calcium traces. The key to judging spiking train authenticity is error estimation. However, due to the lack of an appropriate mathematical model to adequately describe this spike-calcium relationship, little attention has been paid to quantifying error ranges of the reconstructed spike results. By turning attention to the data characteristics close to the reconstruction rather than to a complex mathematic model, we have provided an error estimation method for the reconstructed neuronal spiking from calcium imaging. Real false-negative and false-positive rates of 10 experimental Ca(2+) traces were within the estimated error ranges and confirmed that this evaluation method was effective. Estimation performance of the reconstruction of spikes from calcium transients within a neuronal population demonstrated a reasonable evaluation of the reconstructed spikes without having real electrical signals. These results suggest that our method might be valuable for the quantification of research based on reconstructed neuronal activity, such as to affirm communication between different neurons.
Neuronal spike train entropy estimation by history clustering.
Watters, Nicholas; Reeke, George N
2014-09-01
Neurons send signals to each other by means of sequences of action potentials (spikes). Ignoring variations in spike amplitude and shape that are probably not meaningful to a receiving cell, the information content, or entropy of the signal depends on only the timing of action potentials, and because there is no external clock, only the interspike intervals, and not the absolute spike times, are significant. Estimating spike train entropy is a difficult task, particularly with small data sets, and many methods of entropy estimation have been proposed. Here we present two related model-based methods for estimating the entropy of neural signals and compare them to existing methods. One of the methods is fast and reasonably accurate, and it converges well with short spike time records; the other is impractically time-consuming but apparently very accurate, relying on generating artificial data that are a statistical match to the experimental data. Using the slow, accurate method to generate a best-estimate entropy value, we find that the faster estimator converges to this value more closely and with smaller data sets than many existing entropy estimators.
Generalized analog thresholding for spike acquisition at ultralow sampling rates
He, Bryan D.; Wein, Alex; Varshney, Lav R.; Kusuma, Julius; Richardson, Andrew G.
2015-01-01
Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology. PMID:25904712
Generalized analog thresholding for spike acquisition at ultralow sampling rates.
He, Bryan D; Wein, Alex; Varshney, Lav R; Kusuma, Julius; Richardson, Andrew G; Srinivasan, Lakshminarayan
2015-07-01
Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology. Copyright © 2015 the American Physiological Society.
Unbiased estimation of precise temporal correlations between spike trains.
Stark, Eran; Abeles, Moshe
2009-04-30
A key issue in systems neuroscience is the contribution of precise temporal inter-neuronal interactions to information processing in the brain, and the main analytical tool used for studying pair-wise interactions is the cross-correlation histogram (CCH). Although simple to generate, a CCH is influenced by multiple factors in addition to precise temporal correlations between two spike trains, thus complicating its interpretation. A Monte-Carlo-based technique, the jittering method, has been suggested to isolate the contribution of precise temporal interactions to neural information processing. Here, we show that jittering spike trains is equivalent to convolving the CCH derived from the original trains with a finite window and using a Poisson distribution to estimate probabilities. Both procedures over-fit the original spike trains and therefore the resulting statistical tests are biased and have low power. We devise an alternative method, based on convolving the CCH with a partially hollowed window, and illustrate its utility using artificial and real spike trains. The modified convolution method is unbiased, has high power, and is computationally fast. We recommend caution in the use of the jittering method and in the interpretation of results based on it, and suggest using the modified convolution method for detecting precise temporal correlations between spike trains.
Cao, Ying; Maran, Selva K.; Dhamala, Mukesh; Jaeger, Dieter; Heck, Detlef H.
2012-01-01
Purkinje cells (PCs) in the mammalian cerebellum express high frequency spontaneous activity with average spike rates between 30 and 200 Hz. Cerebellar nuclear (CN) neurons receive converging input from many PCs resulting in a continuous barrage of inhibitory inputs. It has been hypothesized that pauses in PC activity trigger increases in CN spiking activity. A prediction derived from this hypothesis is that pauses in PC simple spike activity represent relevant behavioral or sensory events. Here we asked whether pauses in the simple spike activity of PCs related to either fluid licking or respiration, play a special role in representing information about behavior. Both behaviors are widely represented in cerebellar PC simple spike activity. We recorded PC activity in the vermis and lobus simplex of head fixed mice while monitoring licking and respiratory behavior. Using cross correlation and Granger causality analysis we examined whether short ISIs had a different temporal relation to behavior than long ISIs or pauses. Behavior related simple spike pauses occurred during low-rate simple spike activity in both licking and breathing related PCs. Granger causality analysis revealed causal relationships between simple spike pauses and behavior. However, the same results were obtained from an analysis of surrogate spike trains with gamma ISI distributions constructed to match rate modulations of behavior related Purkinje cells. Our results therefore suggest that the occurrence of pauses in simple spike activity does not represent additional information about behavioral or sensory events that goes beyond the simple spike rate modulations. PMID:22723707
Incorporating spike-rate adaptation into a rate code in mathematical and biological neurons
Ralston, Bridget N.; Flagg, Lucas Q.; Faggin, Eric
2016-01-01
For a slowly varying stimulus, the simplest relationship between a neuron's input and output is a rate code, in which the spike rate is a unique function of the stimulus at that instant. In the case of spike-rate adaptation, there is no unique relationship between input and output, because the spike rate at any time depends both on the instantaneous stimulus and on prior spiking (the “history”). To improve the decoding of spike trains produced by neurons that show spike-rate adaptation, we developed a simple scheme that incorporates “history” into a rate code. We utilized this rate-history code successfully to decode spike trains produced by 1) mathematical models of a neuron in which the mechanism for adaptation (IAHP) is specified, and 2) the gastropyloric receptor (GPR2), a stretch-sensitive neuron in the stomatogastric nervous system of the crab Cancer borealis, that exhibits long-lasting adaptation of unknown origin. Moreover, when we modified the spike rate either mathematically in a model system or by applying neuromodulatory agents to the experimental system, we found that changes in the rate-history code could be related to the biophysical mechanisms responsible for altering the spiking. PMID:26888106
Multi-scale detection of rate changes in spike trains with weak dependencies.
Messer, Michael; Costa, Kauê M; Roeper, Jochen; Schneider, Gaby
2017-04-01
The statistical analysis of neuronal spike trains by models of point processes often relies on the assumption of constant process parameters. However, it is a well-known problem that the parameters of empirical spike trains can be highly variable, such as for example the firing rate. In order to test the null hypothesis of a constant rate and to estimate the change points, a Multiple Filter Test (MFT) and a corresponding algorithm (MFA) have been proposed that can be applied under the assumption of independent inter spike intervals (ISIs). As empirical spike trains often show weak dependencies in the correlation structure of ISIs, we extend the MFT here to point processes associated with short range dependencies. By specifically estimating serial dependencies in the test statistic, we show that the new MFT can be applied to a variety of empirical firing patterns, including positive and negative serial correlations as well as tonic and bursty firing. The new MFT is applied to a data set of empirical spike trains with serial correlations, and simulations show improved performance against methods that assume independence. In case of positive correlations, our new MFT is necessary to reduce the number of false positives, which can be highly enhanced when falsely assuming independence. For the frequent case of negative correlations, the new MFT shows an improved detection probability of change points and thus, also a higher potential of signal extraction from noisy spike trains.
Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain
Brette, Romain
2015-01-01
Does the brain use a firing rate code or a spike timing code? Considering this controversial question from an epistemological perspective, I argue that progress has been hampered by its problematic phrasing. It takes the perspective of an external observer looking at whether those two observables vary with stimuli, and thereby misses the relevant question: which one has a causal role in neural activity? When rephrased in a more meaningful way, the rate-based view appears as an ad hoc methodological postulate, one that is practical but with virtually no empirical or theoretical support. PMID:26617496
Benchmarking Spike Rate Inference in Population Calcium Imaging.
Theis, Lucas; Berens, Philipp; Froudarakis, Emmanouil; Reimer, Jacob; Román Rosón, Miroslav; Baden, Tom; Euler, Thomas; Tolias, Andreas S; Bethge, Matthias
2016-05-04
A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience. Copyright © 2016 Elsevier Inc. All rights reserved.
A memristive spiking neuron with firing rate coding
Ignatov, Marina; Ziegler, Martin; Hansen, Mirko; Petraru, Adrian; Kohlstedt, Hermann
2015-01-01
Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2) and on the chemical electromigration cell Ag/TiO2−x/Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926. PMID:26539074
Negro, Francesco; Yavuz, Ş Utku; Yavuz, Utku Ş; Farina, Dario
2014-01-01
Contractile properties of human motor units provide information on the force capacity and fatigability of muscles. The spike-triggered averaging technique (STA) is a conventional method used to estimate the twitch waveform of single motor units in vivo by averaging the joint force signal. Several limitations of this technique have been previously discussed in an empirical way, using simulated and experimental data. In this study, we provide a theoretical analysis of this technique in the frequency domain and describe its intrinsic limitations. By analyzing the analytical expression of STA, first we show that a certain degree of correlation between the motor unit activities prevents an accurate estimation of the twitch force, even from relatively long recordings. Second, we show that the quality of the twitch estimates by STA is highly related to the relative variability of the inter-spike intervals of motor unit action potentials. Interestingly, if this variability is extremely high, correct estimates could be obtained even for high discharge rates. However, for physiological inter-spike interval variability and discharge rate, the technique performs with relatively low estimation accuracy and high estimation variance. Finally, we show that the selection of the triggers that are most distant from the previous and next, which is often suggested, is not an effective way for improving STA estimates and in some cases can even be detrimental. These results show the intrinsic limitations of the STA technique and provide a theoretical framework for the design of new methods for the measurement of motor unit force twitch.
Separating Spike Count Correlation from Firing Rate Correlation
Vinci, Giuseppe; Ventura, Valérie; Smith, Matthew A.; Kass, Robert E.
2016-01-01
Populations of cortical neurons exhibit shared fluctuations in spiking activity over time. When measured for a pair of neurons over multiple repetitions of an identical stimulus, this phenomenon emerges as correlated trial-to-trial response variability via spike count correlation (SCC). However, spike counts can be viewed as noisy versions of firing rates, which can vary from trial to trial. From this perspective, the SCC for a pair of neurons becomes a noisy version of the corresponding firing-rate correlation (FRC). Furthermore, the magnitude of the SCC is generally smaller than that of the FRC, and is likely to be less sensitive to experimental manipulation. We provide statistical methods for disambiguating time-averaged drive from within-trial noise, thereby separating FRC from SCC. We study these methods to document their reliability, and we apply them to neurons recorded in vivo from area V4, in an alert animal. We show how the various effects we describe are reflected in the data: within-trial effects are largely negligible, while attenuation due to trial-to-trial variation dominates, and frequently produces comparisons in SCC that, because of noise, do not accurately reflect those based on the underlying FRC. PMID:26942746
Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series
Zeldenrust, Fleur; de Knecht, Sicco; Wadman, Wytse J.; Denève, Sophie; Gutkin, Boris
2017-01-01
Understanding the relation between (sensory) stimuli and the activity of neurons (i.e., “the neural code”) lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new (in vitro) method to measure how much information a single neuron transfers from the input it receives to its output spike train. The input is generated by an artificial neural network that responds to a randomly appearing and disappearing “sensory stimulus”: the hidden state. The sum of this network activity is injected as current input into the neuron under investigation. The mutual information between the hidden state on the one hand and spike trains of the artificial network or the recorded spike train on the other hand can easily be estimated due to the binary shape of the hidden state. The characteristics of the input current, such as the time constant as a result of the (dis)appearance rate of the hidden state or the amplitude of the input current (the firing frequency of the neurons in the artificial network), can independently be varied. As an example, we apply this method to pyramidal neurons in the CA1 of mouse hippocampi and compare the recorded spike trains to the optimal response of the “Bayesian neuron” (BN). We conclude that like in the BN, information transfer in hippocampal pyramidal cells is non-linear and amplifying: the information loss between the artificial input and the output spike train is high if the input to the neuron (the firing of the artificial network) is not very informative about the hidden state. If the input to the neuron does contain a lot of information about the hidden state, the information loss is low. Moreover, neurons increase their firing rates in case the (dis)appearance rate is high, so that the (relative) amount of transferred information stays constant. PMID:28663729
Estimating short-term synaptic plasticity from pre- and postsynaptic spiking.
Ghanbari, Abed; Malyshev, Aleksey; Volgushev, Maxim; Stevenson, Ian H
2017-09-01
Short-term synaptic plasticity (STP) critically affects the processing of information in neuronal circuits by reversibly changing the effective strength of connections between neurons on time scales from milliseconds to a few seconds. STP is traditionally studied using intracellular recordings of postsynaptic potentials or currents evoked by presynaptic spikes. However, STP also affects the statistics of postsynaptic spikes. Here we present two model-based approaches for estimating synaptic weights and short-term plasticity from pre- and postsynaptic spike observations alone. We extend a generalized linear model (GLM) that predicts postsynaptic spiking as a function of the observed pre- and postsynaptic spikes and allow the connection strength (coupling term in the GLM) to vary as a function of time based on the history of presynaptic spikes. Our first model assumes that STP follows a Tsodyks-Markram description of vesicle depletion and recovery. In a second model, we introduce a functional description of STP where we estimate the coupling term as a biophysically unrestrained function of the presynaptic inter-spike intervals. To validate the models, we test the accuracy of STP estimation using the spiking of pre- and postsynaptic neurons with known synaptic dynamics. We first test our models using the responses of layer 2/3 pyramidal neurons to simulated presynaptic input with different types of STP, and then use simulated spike trains to examine the effects of spike-frequency adaptation, stochastic vesicle release, spike sorting errors, and common input. We find that, using only spike observations, both model-based methods can accurately reconstruct the time-varying synaptic weights of presynaptic inputs for different types of STP. Our models also capture the differences in postsynaptic spike responses to presynaptic spikes following short vs long inter-spike intervals, similar to results reported for thalamocortical connections. These models may thus be useful
Arata, Hiroki; Mino, Hiroyuki
2012-01-01
This article presents an effect of spontaneous spike firing rates on information transmission of the spike trains in a spherical bushy neuron model of antero-ventral cochlear nuclei. In computer simulations, the synaptic current stimuli ascending from auditory nerve fibers (ANFs) were modeled by a filtered inhomogeneous Poisson process modulated with sinusoidal functions, while the stochastic sodium and stochastic high- and low-threshold potassium channels were incorporated into a single compartment model of the soma in spherical bushy neurons. The information rates were estimated from the entropies of the inter-spike intervals of the spike trains to quantitatively evaluate information transmission in the spherical busy neuron model. The results show that the information rates increased, reached a maximum, and then decreased as the rate of spontaneous spikes from the ANFs increased, implying a resonance phenomenon dependent on the rate of spontaneous spikes from ANFs. In conclusion, this phenomenon similar to the stochastic resonance would be observed due to that spontaneous random spike firings coming from auditory nerves may act as an origin of fluctuation or noise, and these findings may play a key role in the design of better auditory prostheses.
Estimating the correlation between bursty spike trains and local field potentials.
Li, Zhaohui; Ouyang, Gaoxiang; Yao, Li; Li, Xiaoli
2014-09-01
To further understand rhythmic neuronal synchronization, an increasingly useful method is to determine the relationship between the spiking activity of individual neurons and the local field potentials (LFPs) of neural ensembles. Spike field coherence (SFC) is a widely used method for measuring the synchronization between spike trains and LFPs. However, due to the strong dependency of SFC on the burst index, it is not suitable for analyzing the relationship between bursty spike trains and LFPs, particularly in high frequency bands. To address this issue, we developed a method called weighted spike field correlation (WSFC), which uses the first spike in each burst multiple times to estimate the relationship. In the calculation, the number of times that the first spike is used is equal to the spike count per burst. The performance of this method was demonstrated using simulated bursty spike trains and LFPs, which comprised sinusoids with different frequencies, amplitudes, and phases. This method was also used to estimate the correlation between pyramidal cells in the hippocampus and gamma oscillations in rats performing behaviors. Analyses using simulated and real data demonstrated that the WSFC method is a promising measure for estimating the correlation between bursty spike trains and high frequency LFPs.
NASA Astrophysics Data System (ADS)
Quintero-Quiroz, C.; Sorrentino, Taciano; Torrent, M. C.; Masoller, Cristina
2016-04-01
We study the dynamics of semiconductor lasers with optical feedback and direct current modulation, operating in the regime of low frequency fluctuations (LFFs). In the LFF regime the laser intensity displays abrupt spikes: the intensity drops to zero and then gradually recovers. We focus on the inter-spike-intervals (ISIs) and use a method of symbolic time-series analysis, which is based on computing the probabilities of symbolic patterns. We show that the variation of the probabilities of the symbols with the modulation frequency and with the intrinsic spike rate of the laser allows to identify different regimes of noisy locking. Simulations of the Lang-Kobayashi model are in good qualitative agreement with experimental observations.
Speed-invariant encoding of looming object distance requires power law spike rate adaptation.
Clarke, Stephen E; Naud, Richard; Longtin, André; Maler, Leonard
2013-08-13
Neural representations of a moving object's distance and approach speed are essential for determining appropriate orienting responses, such as those observed in the localization behaviors of the weakly electric fish, Apteronotus leptorhynchus. We demonstrate that a power law form of spike rate adaptation transforms an electroreceptor afferent's response to "looming" object motion, effectively parsing information about distance and approach speed into distinct measures of the firing rate. Neurons with dynamics characterized by fixed time scales are shown to confound estimates of object distance and speed. Conversely, power law adaptation modifies an electroreceptor afferent's response according to the time scales present in the stimulus, generating a rate code for looming object distance that is invariant to speed and acceleration. Consequently, estimates of both object distance and approach speed can be uniquely determined from an electroreceptor afferent's firing rate, a multiplexed neural code operating over the extended time scales associated with behaviorally relevant stimuli.
Estimating Temporal Causal Interaction between Spike Trains with Permutation and Transfer Entropy
Li, Zhaohui; Li, Xiaoli
2013-01-01
Estimating the causal interaction between neurons is very important for better understanding the functional connectivity in neuronal networks. We propose a method called normalized permutation transfer entropy (NPTE) to evaluate the temporal causal interaction between spike trains, which quantifies the fraction of ordinal information in a neuron that has presented in another one. The performance of this method is evaluated with the spike trains generated by an Izhikevich’s neuronal model. Results show that the NPTE method can effectively estimate the causal interaction between two neurons without influence of data length. Considering both the precision of time delay estimated and the robustness of information flow estimated against neuronal firing rate, the NPTE method is superior to other information theoretic method including normalized transfer entropy, symbolic transfer entropy and permutation conditional mutual information. To test the performance of NPTE on analyzing simulated biophysically realistic synapses, an Izhikevich’s cortical network that based on the neuronal model is employed. It is found that the NPTE method is able to characterize mutual interactions and identify spurious causality in a network of three neurons exactly. We conclude that the proposed method can obtain more reliable comparison of interactions between different pairs of neurons and is a promising tool to uncover more details on the neural coding. PMID:23940662
Estimating Extracellular Spike Waveforms from CA1 Pyramidal Cells with Multichannel Electrodes
Molden, Sturla; Moldestad, Olve; Storm, Johan F.
2013-01-01
Extracellular (EC) recordings of action potentials from the intact brain are embedded in background voltage fluctuations known as the “local field potential” (LFP). In order to use EC spike recordings for studying biophysical properties of neurons, the spike waveforms must be separated from the LFP. Linear low-pass and high-pass filters are usually insufficient to separate spike waveforms from LFP, because they have overlapping frequency bands. Broad-band recordings of LFP and spikes were obtained with a 16-channel laminar electrode array (silicone probe). We developed an algorithm whereby local LFP signals from spike-containing channel were modeled using locally weighted polynomial regression analysis of adjoining channels without spikes. The modeled LFP signal was subtracted from the recording to estimate the embedded spike waveforms. We tested the method both on defined spike waveforms added to LFP recordings, and on in vivo-recorded extracellular spikes from hippocampal CA1 pyramidal cells in anaesthetized mice. We show that the algorithm can correctly extract the spike waveforms embedded in the LFP. In contrast, traditional high-pass filters failed to recover correct spike shapes, albeit produceing smaller standard errors. We found that high-pass RC or 2-pole Butterworth filters with cut-off frequencies below 12.5 Hz, are required to retrieve waveforms comparable to our method. The method was also compared to spike-triggered averages of the broad-band signal, and yielded waveforms with smaller standard errors and less distortion before and after the spike. PMID:24391714
Graupner, Michael; Wallisch, Pascal; Ostojic, Srdjan
2016-11-02
Synaptic plasticity is sensitive to the rate and the timing of presynaptic and postsynaptic action potentials. In experimental protocols inducing plasticity, the imposed spike trains are typically regular and the relative timing between every presynaptic and postsynaptic spike is fixed. This is at odds with firing patterns observed in the cortex of intact animals, where cells fire irregularly and the timing between presynaptic and postsynaptic spikes varies. To investigate synaptic changes elicited by in vivo-like firing, we used numerical simulations and mathematical analysis of synaptic plasticity models. We found that the influence of spike timing on plasticity is weaker than expected from regular stimulation protocols. Moreover, when neurons fire irregularly, synaptic changes induced by precise spike timing can be equivalently induced by a modest firing rate variation. Our findings bridge the gap between existing results on synaptic plasticity and plasticity occurring in vivo, and challenge the dominant role of spike timing in plasticity.
Estimating entropy rates with Bayesian confidence intervals.
Kennel, Matthew B; Shlens, Jonathon; Abarbanel, Henry D I; Chichilnisky, E J
2005-07-01
The entropy rate quantifies the amount of uncertainty or disorder produced by any dynamical system. In a spiking neuron, this uncertainty translates into the amount of information potentially encoded and thus the subject of intense theoretical and experimental investigation. Estimating this quantity in observed, experimental data is difficult and requires a judicious selection of probabilistic models, balancing between two opposing biases. We use a model weighting principle originally developed for lossless data compression, following the minimum description length principle. This weighting yields a direct estimator of the entropy rate, which, compared to existing methods, exhibits significantly less bias and converges faster in simulation. With Monte Carlo techinques, we estimate a Bayesian confidence interval for the entropy rate. In related work, we apply these ideas to estimate the information rates between sensory stimuli and neural responses in experimental data (Shlens, Kennel, Abarbanel, & Chichilnisky, in preparation).
Dorval, Alan D
2008-08-15
The maximal information that the spike train of any neuron can pass on to subsequent neurons can be quantified as the neuronal firing pattern entropy. Difficulties associated with estimating entropy from small datasets have proven an obstacle to the widespread reporting of firing pattern entropies and more generally, the use of information theory within the neuroscience community. In the most accessible class of entropy estimation techniques, spike trains are partitioned linearly in time and entropy is estimated from the probability distribution of firing patterns within a partition. Ample previous work has focused on various techniques to minimize the finite dataset bias and standard deviation of entropy estimates from under-sampled probability distributions on spike timing events partitioned linearly in time. In this manuscript we present evidence that all distribution-based techniques would benefit from inter-spike intervals being partitioned in logarithmic time. We show that with logarithmic partitioning, firing rate changes become independent of firing pattern entropy. We delineate the entire entropy estimation process with two example neuronal models, demonstrating the robust improvements in bias and standard deviation that the logarithmic time method yields over two widely used linearly partitioned time approaches.
Pillow, Jonathan W; Ahmadian, Yashar; Paninski, Liam
2011-01-01
One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.
Deneux, Thomas; Kaszas, Attila; Szalay, Gergely; Katona, Gergely; Lakner, Tamás; Grinvald, Amiram; Rózsa, Balázs; Vanzetta, Ivo
2016-01-01
Extracting neuronal spiking activity from large-scale two-photon recordings remains challenging, especially in mammals in vivo, where large noises often contaminate the signals. We propose a method, MLspike, which returns the most likely spike train underlying the measured calcium fluorescence. It relies on a physiological model including baseline fluctuations and distinct nonlinearities for synthetic and genetically encoded indicators. Model parameters can be either provided by the user or estimated from the data themselves. MLspike is computationally efficient thanks to its original discretization of probability representations; moreover, it can also return spike probabilities or samples. Benchmarked on extensive simulations and real data from seven different preparations, it outperformed state-of-the-art algorithms. Combined with the finding obtained from systematic data investigation (noise level, spiking rate and so on) that photonic noise is not necessarily the main limiting factor, our method allows spike extraction from large-scale recordings, as demonstrated on acousto-optical three-dimensional recordings of over 1,000 neurons in vivo. PMID:27432255
Speed-invariant encoding of looming object distance requires power law spike rate adaptation
Clarke, Stephen E.; Naud, Richard; Longtin, André; Maler, Leonard
2013-01-01
Neural representations of a moving object’s distance and approach speed are essential for determining appropriate orienting responses, such as those observed in the localization behaviors of the weakly electric fish, Apteronotus leptorhynchus. We demonstrate that a power law form of spike rate adaptation transforms an electroreceptor afferent’s response to “looming” object motion, effectively parsing information about distance and approach speed into distinct measures of the firing rate. Neurons with dynamics characterized by fixed time scales are shown to confound estimates of object distance and speed. Conversely, power law adaptation modifies an electroreceptor afferent’s response according to the time scales present in the stimulus, generating a rate code for looming object distance that is invariant to speed and acceleration. Consequently, estimates of both object distance and approach speed can be uniquely determined from an electroreceptor afferent’s firing rate, a multiplexed neural code operating over the extended time scales associated with behaviorally relevant stimuli. PMID:23898185
NASA Astrophysics Data System (ADS)
Shimazaki, Hideaki
2013-12-01
Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model) with a state-space method (Shimazaki et al. 2012 PLoS Comput Biol 8 e1002385). This method allows us to estimate stimulus-evoked dynamics of neuronal interactions which is reproducible in repeated trials under identical experimental conditions. However, the method may not be suitable for detecting stimulus responses if the neuronal dynamics exhibits significant variability across trials. In addition, the previous model does not include effects of past spiking activity of the neurons on the current state of ensemble activity. In this study, we develop a parametric method for simultaneously estimating the stimulus and spike-history effects on the ensemble activity from single-trial data even if the neurons exhibit dynamics that is largely unrelated to these effects. For this goal, we model ensemble neuronal activity as a latent process and include the stimulus and spike-history effects as exogenous inputs to the latent process. We develop an expectation-maximization algorithm that simultaneously achieves estimation of the latent process, stimulus responses, and spike-history effects. The proposed method is useful to analyze an interaction of internal cortical states and sensory evoked activity.
Adaptive learning rate of SpikeProp based on weight convergence analysis.
Shrestha, Sumit Bam; Song, Qing
2015-03-01
A Spiking Neural Network (SNN) training using SpikeProp and its variants is usually affected by sudden rise in learning cost called surges. These surges cause diversion in the learning process and often cause it to fail as well. Researches have shown that proper learning rate is crucial to avoid these surges. In this paper, we perform weight convergence analysis to determine the proper step size in each iteration of weight update and derive an adaptive learning rate extension to SpikeProp that assures convergence of the learning process. We have analyzed the performance of this learning rate adaptation with existing methods via simulations on different benchmarks. The results show that using adaptive learning rate significantly improves the weight convergence and speeds up learning as well. Copyright © 2014 Elsevier Ltd. All rights reserved.
NASA Astrophysics Data System (ADS)
Liao, Yuxi; She, Xiwei; Wang, Yiwen; Zhang, Shaomin; Zhang, Qiaosheng; Zheng, Xiaoxiang; Principe, Jose C.
2015-12-01
Objective. Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. Approach. In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat’s motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. Main results. Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. Significance. These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.
NASA Astrophysics Data System (ADS)
Frenkel, M. M.; LaVigne, M.; Miller, H. R.; Hill, T. M.; McNichol, A.; Gaylord, M. Lardie
2017-07-01
for all live-collected corals with complete Δ14C bomb spikes. Hence, this study provides paleoceanographers utilizing bamboo corals with a method for reducing age model uncertainty within the anthropogenic bomb spike era ( 1957-present). Chronological uncertainty is larger for the earliest portion of coral growth, particularly for skeleton precipitated prior to bomb spike tie points, meaning age estimations for samples living before 1957 remain uncertain. Combining this technique with additional chronological markers could improve age models for an entire bamboo coral. Finally, the relative consistency in growth rate in similarly-aged corals of the same depth and location supports the hypothesis that skeletal growth may be limited by local environmental conditions.
A spike correction approach for variability analysis of heart rate sick infants
NASA Astrophysics Data System (ADS)
Govindan, R. B.; Al-Shargabi, Tareq; Metzler, Marina; Andescavage, Nickie N.; Joshi, Radhika; du Plessis, Adré
2016-02-01
In critical care monitoring, the heart rate (HR) offers valuable insight into the autonomic function of sick infants. However, the intensity of monitoring and clinical care such as intubation, suctioning, and venesection as well as routine movement, create a hostile environment for contamination of continuous signals. These artifacts usually present as spikes in the HR signal, which interfere with the characterization and subsequent evaluation of the HR. Post hoc spike removal is commonly required in research studies but is not feasible in clinical monitoring. We propose a two-step process to correct spikes in HR data. Step 1 comprises of two sub-steps to remove the spikes with upward deflection and downward deflection. In Step 2, we repeat Step 1, for different ɛ values and calculate root mean square (RMS) of the difference between the uncorrected HR and the corrected HR. The corrected HR that displayed either the smallest RMS value or the same RMS values for two or more ɛ values is considered optimally corrected data. We demonstrate the application of this approach to HR data collected from 5 preterm infants. We show that there is a significant difference between the spectral powers obtained for spike uncorrected and spike corrected HR.
Associative Memory Neural Network with Low Temporal Spiking Rates
NASA Astrophysics Data System (ADS)
Amit, Daniel J.; Treves, A.
1989-10-01
We describe a modified attractor neural network in which neuronal dynamics takes place on a time scale of the absolute refractory period but the mean temporal firing rate of any neuron in the network is lower by an arbitrary factor that characterizes the strength of the effective inhibition. It operates by encoding information on the excitatory neurons only and assuming the inhibitory neurons to be faster and to inhibit the excitatory ones by an effective postsynaptic potential that is expressed in terms of the activity of the excitatory neurons themselves. Retrieval is identified as a nonergodic behavior of the network whose consecutive states have a significantly enhanced activity rate for the neurons that should be active in a stored pattern and a reduced activity rate for the neurons that are inactive in the memorized pattern. In contrast to the Hopfield model the network operates away from fixed points and under the strong influence of noise. As a consequence, of the neurons that should be active in a pattern, only a small fraction is active in any given time cycle and those are randomly distributed, leading to reduced temporal rates. We argue that this model brings neural network models much closer to biological reality. We present the results of detailed analysis of the model as well as simulations.
Parameter estimation in spiking neural networks: a reverse-engineering approach.
Rostro-Gonzalez, H; Cessac, B; Vieville, T
2012-04-01
This paper presents a reverse engineering approach for parameter estimation in spiking neural networks (SNNs). We consider the deterministic evolution of a time-discretized network with spiking neurons, where synaptic transmission has delays, modeled as a neural network of the generalized integrate and fire type. Our approach aims at by-passing the fact that the parameter estimation in SNN results in a non-deterministic polynomial-time hard problem when delays are to be considered. Here, this assumption has been reformulated as a linear programming (LP) problem in order to perform the solution in a polynomial time. Besides, the LP problem formulation makes the fact that the reverse engineering of a neural network can be performed from the observation of the spike times explicit. Furthermore, we point out how the LP adjustment mechanism is local to each neuron and has the same structure as a 'Hebbian' rule. Finally, we present a generalization of this approach to the design of input-output (I/O) transformations as a practical method to 'program' a spiking network, i.e. find a set of parameters allowing us to exactly reproduce the network output, given an input. Numerical verifications and illustrations are provided.
Wallisch, Pascal; Ostojic, Srdjan
2016-01-01
Synaptic plasticity is sensitive to the rate and the timing of presynaptic and postsynaptic action potentials. In experimental protocols inducing plasticity, the imposed spike trains are typically regular and the relative timing between every presynaptic and postsynaptic spike is fixed. This is at odds with firing patterns observed in the cortex of intact animals, where cells fire irregularly and the timing between presynaptic and postsynaptic spikes varies. To investigate synaptic changes elicited by in vivo-like firing, we used numerical simulations and mathematical analysis of synaptic plasticity models. We found that the influence of spike timing on plasticity is weaker than expected from regular stimulation protocols. Moreover, when neurons fire irregularly, synaptic changes induced by precise spike timing can be equivalently induced by a modest firing rate variation. Our findings bridge the gap between existing results on synaptic plasticity and plasticity occurring in vivo, and challenge the dominant role of spike timing in plasticity. SIGNIFICANCE STATEMENT Synaptic plasticity, the change in efficacy of connections between neurons, is thought to underlie learning and memory. The dominant paradigm posits that the precise timing of neural action potentials (APs) is central for plasticity induction. This concept is based on experiments using highly regular and stereotyped patterns of APs, in stark contrast with natural neuronal activity. Using synaptic plasticity models, we investigated how irregular, in vivo-like activity shapes synaptic plasticity. We found that synaptic changes induced by precise timing of APs are much weaker than suggested by regular stimulation protocols, and can be equivalently induced by modest variations of the AP rate alone. Our results call into question the dominant role of precise AP timing for plasticity in natural conditions. PMID:27807166
Goodness-of-Fit Tests and Nonparametric Adaptive Estimation for Spike Train Analysis
2014-01-01
When dealing with classical spike train analysis, the practitioner often performs goodness-of-fit tests to test whether the observed process is a Poisson process, for instance, or if it obeys another type of probabilistic model (Yana et al. in Biophys. J. 46(3):323–330, 1984; Brown et al. in Neural Comput. 14(2):325–346, 2002; Pouzat and Chaffiol in Technical report, http://arxiv.org/abs/arXiv:0909.2785, 2009). In doing so, there is a fundamental plug-in step, where the parameters of the supposed underlying model are estimated. The aim of this article is to show that plug-in has sometimes very undesirable effects. We propose a new method based on subsampling to deal with those plug-in issues in the case of the Kolmogorov–Smirnov test of uniformity. The method relies on the plug-in of good estimates of the underlying model that have to be consistent with a controlled rate of convergence. Some nonparametric estimates satisfying those constraints in the Poisson or in the Hawkes framework are highlighted. Moreover, they share adaptive properties that are useful from a practical point of view. We show the performance of those methods on simulated data. We also provide a complete analysis with these tools on single unit activity recorded on a monkey during a sensory-motor task. Electronic Supplementary Material The online version of this article (doi:10.1186/2190-8567-4-3) contains supplementary material. PMID:24742008
Goodness-of-Fit Tests and Nonparametric Adaptive Estimation for Spike Train Analysis.
Reynaud-Bouret, Patricia; Rivoirard, Vincent; Grammont, Franck; Tuleau-Malot, Christine
2014-04-17
When dealing with classical spike train analysis, the practitioner often performs goodness-of-fit tests to test whether the observed process is a Poisson process, for instance, or if it obeys another type of probabilistic model (Yana et al. in Biophys. J. 46(3):323-330, 1984; Brown et al. in Neural Comput. 14(2):325-346, 2002; Pouzat and Chaffiol in Technical report, http://arxiv.org/abs/arXiv:0909.2785, 2009). In doing so, there is a fundamental plug-in step, where the parameters of the supposed underlying model are estimated. The aim of this article is to show that plug-in has sometimes very undesirable effects. We propose a new method based on subsampling to deal with those plug-in issues in the case of the Kolmogorov-Smirnov test of uniformity. The method relies on the plug-in of good estimates of the underlying model that have to be consistent with a controlled rate of convergence. Some nonparametric estimates satisfying those constraints in the Poisson or in the Hawkes framework are highlighted. Moreover, they share adaptive properties that are useful from a practical point of view. We show the performance of those methods on simulated data. We also provide a complete analysis with these tools on single unit activity recorded on a monkey during a sensory-motor task.Electronic Supplementary MaterialThe online version of this article (doi:10.1186/2190-8567-4-3) contains supplementary material.
Hoang, Huu; Yamashita, Okito; Tokuda, Isao T; Sato, Masa-Aki; Kawato, Mitsuo; Toyama, Keisuke
2015-01-01
The inverse problem for estimating model parameters from brain spike data is an ill-posed problem because of a huge mismatch in the system complexity between the model and the brain as well as its non-stationary dynamics, and needs a stochastic approach that finds the most likely solution among many possible solutions. In the present study, we developed a segmental Bayesian method to estimate the two parameters of interest, the gap-junctional (gc ) and inhibitory conductance (gi ) from inferior olive spike data. Feature vectors were estimated for the spike data in a segment-wise fashion to compensate for the non-stationary firing dynamics. Hierarchical Bayesian estimation was conducted to estimate the gc and gi for every spike segment using a forward model constructed in the principal component analysis (PCA) space of the feature vectors, and to merge the segmental estimates into single estimates for every neuron. The segmental Bayesian estimation gave smaller fitting errors than the conventional Bayesian inference, which finds the estimates once across the entire spike data, or the minimum error method, which directly finds the closest match in the PCA space. The segmental Bayesian inference has the potential to overcome the problem of non-stationary dynamics and resolve the ill-posedness of the inverse problem because of the mismatch between the model and the brain under the constraints based, and it is a useful tool to evaluate parameters of interest for neuroscience from experimental spike train data.
Knowlton, Chris; Meliza, C Daniel; Margoliash, Daniel; Abarbanel, Henry D I
2014-06-01
Estimating the behavior of a network of neurons requires accurate models of the individual neurons along with accurate characterizations of the connections among them. Whereas for a single cell, measurements of the intracellular voltage are technically feasible and sufficient to characterize a useful model of its behavior, making sufficient numbers of simultaneous intracellular measurements to characterize even small networks is infeasible. This paper builds on prior work on single neurons to explore whether knowledge of the time of spiking of neurons in a network, once the nodes (neurons) have been characterized biophysically, can provide enough information to usefully constrain the functional architecture of the network: the existence of synaptic links among neurons and their strength. Using standardized voltage and synaptic gating variable waveforms associated with a spike, we demonstrate that the functional architecture of a small network of model neurons can be established.
Glascoe, E
2008-08-11
It is estimated that PBXN-110 will burn laminarly with a burn function of B = (0.6-1.3)*P{sup 1.0} (B is the burn rate in mm/s and P is pressure in MPa). This paper provides a brief discussion of how this burn behavior was estimated.
Spike Phase Locking in CA1 Pyramidal Neurons depends on Background Conductance and Firing Rate
Broiche, Tilman; Malerba, Paola; Dorval, Alan D.; Borisyuk, Alla; Fernandez, Fernando R.; White, John A.
2012-01-01
Oscillatory activity in neuronal networks correlates with different behavioral states throughout the nervous system, and the frequency-response characteristics of individual neurons are believed to be critical for network oscillations. Recent in vivo studies suggest that neurons experience periods of high membrane conductance, and that action potentials are often driven by membrane-potential fluctuations in the living animal. To investigate the frequency-response characteristics of CA1 pyramidal neurons in the presence of high conductance and voltage fluctuations, we performed dynamic-clamp experiments in rat hippocampal brain slices. We drove neurons with noisy stimuli that included a sinusoidal component ranging, in different trials, from 0.1 to 500 Hz. In subsequent data analysis, we determined action potential phase-locking profiles with respect to background conductance, average firing rate, and frequency of the sinusoidal component. We found that background conductance and firing rate qualitatively change the phase-locking profiles of CA1 pyramidal neurons vs. frequency. In particular, higher average spiking rates promoted band-pass profiles, and the high-conductance state promoted phase-locking at frequencies well above what would be predicted from changes in the membrane time constant. Mechanistically, spike-rate adaptation and frequency resonance in the spike-generating mechanism are implicated in shaping the different phase-locking profiles. Our results demonstrate that CA1 pyramidal cells can actively change their synchronization properties in response to global changes in activity associated with different behavioral states. PMID:23055508
A neuromorphic VLSI design for spike timing and rate based synaptic plasticity.
Rahimi Azghadi, Mostafa; Al-Sarawi, Said; Abbott, Derek; Iannella, Nicolangelo
2013-09-01
Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is capable of reproducing the outcomes from a variety of biological experiments, while the PSTDP rule fails to reproduce them. Additionally, it has been shown that the behaviour inherent to the spike rate-based Bienenstock-Cooper-Munro (BCM) synaptic plasticity rule can also emerge from the TSTDP rule. This paper proposes an analogue implementation of the TSTDP rule. The proposed VLSI circuit has been designed using the AMS 0.35 μm CMOS process and has been simulated using design kits for Synopsys and Cadence tools. Simulation results demonstrate how well the proposed circuit can alter synaptic weights according to the timing difference amongst a set of different patterns of spikes. Furthermore, the circuit is shown to give rise to a BCM-like learning rule, which is a rate-based rule. To mimic an implementation environment, a 1000 run Monte Carlo (MC) analysis was conducted on the proposed circuit. The presented MC simulation analysis and the simulation result from fine-tuned circuits show that it is possible to mitigate the effect of process variations in the proof of concept circuit; however, a practical variation aware design technique is required to promise a high circuit performance in a large scale neural network. We believe that the proposed design can play a significant role in future VLSI implementations of both spike timing and rate based neuromorphic learning systems.
Lyamzin, Dmitry R; Macke, Jakob H; Lesica, Nicholas A
2010-01-01
As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modeling such data must also be developed. Here, we present a model for the type of data commonly recorded in early sensory pathways: responses to repeated trials of a sensory stimulus in which each neuron has it own time-varying spike rate (as described by its PSTH) and the dependencies between cells are characterized by both signal and noise correlations. This model is an extension of previous attempts to model population spike trains designed to control only the total correlation between cells. In our model, the response of each cell is represented as a binary vector given by the dichotomized sum of a deterministic "signal" that is repeated on each trial and a Gaussian random "noise" that is different on each trial. This model allows the simulation of population spike trains with PSTHs, trial-to-trial variability, and pairwise correlations that match those measured experimentally. Furthermore, the model also allows the noise correlations in the spike trains to be manipulated independently of the signal correlations and single-cell properties. To demonstrate the utility of the model, we use it to simulate and manipulate experimental responses from the mammalian auditory and visual systems. We also present a general form of the model in which both the signal and noise are Gaussian random processes, allowing the mean spike rate, trial-to-trial variability, and pairwise signal and noise correlations to be specified independently. Together, these methods for modeling spike trains comprise a potentially powerful set of tools for both theorists and experimentalists studying population responses in sensory systems.
NASA Astrophysics Data System (ADS)
Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan
2016-07-01
This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design.
Estimation of spontaneous mutation rates.
Natarajan, Loki; Berry, Charles C; Gasche, Christoph
2003-09-01
Spontaneous or randomly occurring mutations play a key role in cancer progression. Estimation of the mutation rate of cancer cells can provide useful information about the disease. To ascertain these mutation rates, we need mathematical models that describe the distribution of mutant cells. In this investigation, we develop a discrete time stochastic model for a mutational birth process. We assume that mutations occur concurrently with mitosis so that when a nonmutant parent cell splits into two progeny, one of these daughter cells could carry a mutation. We propose an estimator for the mutation rate and investigate its statistical properties via theory and simulations. A salient feature of this estimator is the ease with which it can be computed. The methods developed herein are applied to a human colorectal cancer cell line and compared to existing continuous time models.
Effect of marital status on death rates. Part 2: Transient mortality spikes
NASA Astrophysics Data System (ADS)
Richmond, Peter; Roehner, Bertrand M.
2016-05-01
We examine what happens in a population when it experiences an abrupt change in surrounding conditions. Several cases of such "abrupt transitions" for both physical and living social systems are analyzed from which it can be seen that all share a common pattern. First, a steep rising death rate followed by a much slower relaxation process during which the death rate decreases as a power law. This leads us to propose a general principle which can be summarized as follows: "Any abrupt change in living conditions generates a mortality spike which acts as a kind of selection process". This we term the Transient Shock conjecture. It provides a qualitative model which leads to testable predictions. For example, marriage certainly brings about a major change in personal and social conditions and according to our conjecture one would expect a mortality spike in the months following marriage. At first sight this may seem an unlikely proposition but we demonstrate (by three different methods) that even here the existence of mortality spikes is supported by solid empirical evidence.
Encoding Odorant Identity by Spiking Packets of Rate-Invariant Neurons in Awake Mice
Gschwend, Olivier; Beroud, Jonathan; Carleton, Alan
2012-01-01
Background How do neural networks encode sensory information? Following sensory stimulation, neural coding is commonly assumed to be based on neurons changing their firing rate. In contrast, both theoretical works and experiments in several sensory systems showed that neurons could encode information as coordinated cell assemblies by adjusting their spike timing and without changing their firing rate. Nevertheless, in the olfactory system, there is little experimental evidence supporting such model. Methodology/Principal Findings To study these issues, we implanted tetrodes in the olfactory bulb of awake mice to record the odorant-evoked activity of mitral/tufted (M/T) cells. We showed that following odorant presentation, most M/T neurons do not significantly change their firing rate over a breathing cycle but rather respond to odorant stimulation by redistributing their firing activity within respiratory cycles. In addition, we showed that sensory information can be encoded by cell assemblies composed of such neurons, thus supporting the idea that coordinated populations of globally rate-invariant neurons could be efficiently used to convey information about the odorant identity. We showed that different coding schemes can convey high amount of odorant information for specific read-out time window. Finally we showed that the optimal readout time window corresponds to the duration of gamma oscillations cycles. Conclusion We propose that odorant can be encoded by population of cells that exhibit fine temporal tuning of spiking activity while displaying weak or no firing rate change. These cell assemblies may transfer sensory information in spiking packets sequence using the gamma oscillations as a clock. This would allow the system to reach a tradeoff between rapid and accurate odorant discrimination. PMID:22272291
Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.
Ly, Cheng
2015-12-01
Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a combination of Monte Carlo simulations and probability density function equations. We find that the relationship between intrinsic and network heterogeneity has a strong effect on the overall level of heterogeneity of the firing rates. Specifically, this relationship can lead to amplification or attenuation of firing rate heterogeneity, and these effects depend on whether the recurrent network is firing asynchronously or rhythmically firing. These observations are captured with the aforementioned reduction method, and furthermore simpler analytic descriptions based on this dimension reduction method are developed. The final analytic descriptions provide compact and descriptive formulas for how the relationship between intrinsic and network heterogeneity determines the firing rate heterogeneity dynamics in various settings.
Cluster-based analysis improves predictive validity of spike-triggered receptive field estimates.
Bigelow, James; Malone, Brian J
2017-01-01
Spectrotemporal receptive field (STRF) characterization is a central goal of auditory physiology. STRFs are often approximated by the spike-triggered average (STA), which reflects the average stimulus preceding a spike. In many cases, the raw STA is subjected to a threshold defined by gain values expected by chance. However, such correction methods have not been universally adopted, and the consequences of specific gain-thresholding approaches have not been investigated systematically. Here, we evaluate two classes of statistical correction techniques, using the resulting STRF estimates to predict responses to a novel validation stimulus. The first, more traditional technique eliminated STRF pixels (time-frequency bins) with gain values expected by chance. This correction method yielded significant increases in prediction accuracy, including when the threshold setting was optimized for each unit. The second technique was a two-step thresholding procedure wherein clusters of contiguous pixels surviving an initial gain threshold were then subjected to a cluster mass threshold based on summed pixel values. This approach significantly improved upon even the best gain-thresholding techniques. Additional analyses suggested that allowing threshold settings to vary independently for excitatory and inhibitory subfields of the STRF resulted in only marginal additional gains, at best. In summary, augmenting reverse correlation techniques with principled statistical correction choices increased prediction accuracy by over 80% for multi-unit STRFs and by over 40% for single-unit STRFs, furthering the interpretational relevance of the recovered spectrotemporal filters for auditory systems analysis.
Effects of sodium salicylate on spontaneous and evoked spike rate in the dorsal cochlear nucleus
Wei, Lei; Ding, Dalian; Sun, Wei; Xu-Friedman, Matthew A.; Salvi, Richard
2010-01-01
Spontaneous hyperactivity in the dorsal cochlear nucleus (DCN), particularly in fusiform cells, has been proposed as a neural generator of tinnitus. To determine if sodium salicylate, a reliable tinnitus inducer, could evoke hyperactivity in the DCN, we measured the spontaneous and depolarization-evoked spike rate in fusiform and cartwheel cells during salicylate superfusion. Five minute treatment with 1.4 mM salicylate suppressed spontaneous and evoked firing in fusiform cells; this decrease partially recovered after salicylate washout. Less suppression and greater recovery occurred with 3 minute treatment using 1.4 mM salicylate. In contrast, salicylate had no effect on the spontaneous or evoked firing of cartwheel cells indicating that salicylate’s suppressive effects are specific to fusiform cells. To determine if salicylate’s suppressive effects were a consequence of increased synaptic inhibition, spontaneous inhibitory post-synaptic currents (IPSC) were measured during salicylate treatment. Salicylate unexpectedly reduced IPSC thereby ruling out increased inhibition as a mechanism to explain the depressed firing rates in fusiform cells. The salicylate-induced suppression of fusiform spike rate apparently arises from unidentified changes in the cell’s intrinsic excitability. PMID:20430089
Wester, Jason C; Contreras, Diego
2013-08-01
Spike threshold filters incoming inputs and thus gates activity flow through neuronal networks. Threshold is variable, and in many types of neurons there is a relationship between the threshold voltage and the rate of rise of the membrane potential (dVm/dt) leading to the spike. In primary sensory cortex this relationship enhances the sensitivity of neurons to a particular stimulus feature. While Na⁺ channel inactivation may contribute to this relationship, recent evidence indicates that K⁺ currents located in the spike initiation zone are crucial. Here we used a simple Hodgkin-Huxley biophysical model to systematically investigate the role of K⁺ and Na⁺ current parameters (activation voltages and kinetics) in regulating spike threshold as a function of dVm/dt. Threshold was determined empirically and not estimated from the shape of the Vm prior to a spike. This allowed us to investigate intrinsic currents and values of gating variables at the precise voltage threshold. We found that Na⁺ nactivation is sufficient to produce the relationship provided it occurs at hyperpolarized voltages combined with slow kinetics. Alternatively, hyperpolarization of the K⁺ current activation voltage, even in the absence of Na⁺ inactivation, is also sufficient to produce the relationship. This hyperpolarized shift of K⁺ activation allows an outward current prior to spike initiation to antagonize the Na⁺ inward current such that it becomes self-sustaining at a more depolarized voltage. Our simulations demonstrate parameter constraints on Na⁺ inactivation and the biophysical mechanism by which an outward current regulates spike threshold as a function of dVm/dt.
A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity.
Rachmuth, Guy; Shouval, Harel Z; Bear, Mark F; Poon, Chi-Sang
2011-12-06
Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.
The dependence of spike field coherence on expected intensity.
Lepage, Kyle Q; Kramer, Mark A; Eden, Uri T
2011-09-01
The coherence between neural spike trains and local-field potential recordings, called spike-field coherence, is of key importance in many neuroscience studies. In this work, aside from questions of estimator performance, we demonstrate that theoretical spike-field coherence for a broad class of spiking models depends on the expected rate of spiking. This rate dependence confounds the phase locking of spike events to field-potential oscillations with overall neuron activity and is demonstrated analytically, for a large class of stochastic models, and in simulation. Finally, the relationship between the spike-field coherence and the intensity field coherence is detailed analytically. This latter quantity is independent of neuron firing rate and, under commonly found conditions, is proportional to the probability that a neuron spikes at a specific phase of field oscillation. Hence, intensity field coherence is a rate-independent measure and a candidate on which to base the appropriate statistical inference of spike field synchrony.
Wirtzfeld, Michael R; Ibrahim, Rasha A; Bruce, Ian C
2017-07-26
Perceptual studies of speech intelligibility have shown that slow variations of acoustic envelope (ENV) in a small set of frequency bands provides adequate information for good perceptual performance in quiet, whereas acoustic temporal fine-structure (TFS) cues play a supporting role in background noise. However, the implications for neural coding are prone to misinterpretation because the mean-rate neural representation can contain recovered ENV cues from cochlear filtering of TFS. We investigated ENV recovery and spike-time TFS coding using objective measures of simulated mean-rate and spike-timing neural representations of chimaeric speech, in which either the ENV or the TFS is replaced by another signal. We (a) evaluated the levels of mean-rate and spike-timing neural information for two categories of chimaeric speech, one retaining ENV cues and the other TFS; (b) examined the level of recovered ENV from cochlear filtering of TFS speech; (c) examined and quantified the contribution to recovered ENV from spike-timing cues using a lateral inhibition network (LIN); and (d) constructed linear regression models with objective measures of mean-rate and spike-timing neural cues and subjective phoneme perception scores from normal-hearing listeners. The mean-rate neural cues from the original ENV and recovered ENV partially accounted for perceptual score variability, with additional variability explained by the recovered ENV from the LIN-processed TFS speech. The best model predictions of chimaeric speech intelligibility were found when both the mean-rate and spike-timing neural cues were included, providing further evidence that spike-time coding of TFS cues is important for intelligibility when the speech envelope is degraded.
Onizuka, Miho; Hoang, Huu; Kawato, Mitsuo; Tokuda, Isao T; Schweighofer, Nicolas; Katori, Yuichi; Aihara, Kazuyuki; Lang, Eric J; Toyama, Keisuke
2013-11-01
The inferior olive (IO) possesses synaptic glomeruli, which contain dendritic spines from neighboring neurons and presynaptic terminals, many of which are inhibitory and GABAergic. Gap junctions between the spines electrically couple neighboring neurons whereas the GABAergic synaptic terminals are thought to act to decrease the effectiveness of this coupling. Thus, the glomeruli are thought to be important for determining the oscillatory and synchronized activity displayed by IO neurons. Indeed, the tendency to display such activity patterns is enhanced or reduced by the local administration of the GABA-A receptor blocker picrotoxin (PIX) or the gap junction blocker carbenoxolone (CBX), respectively. We studied the functional roles of the glomeruli by solving the inverse problem of estimating the inhibitory (gi) and gap-junctional conductance (gc) using an IO network model. This model was built upon a prior IO network model, in which the individual neurons consisted of soma and dendritic compartments, by adding a glomerular compartment comprising electrically coupled spines that received inhibitory synapses. The model was used in the forward mode to simulate spike data under PIX and CBX conditions for comparison with experimental data consisting of multi-electrode recordings of complex spikes from arrays of Purkinje cells (complex spikes are generated in a one-to-one manner by IO spikes and thus can substitute for directly measuring IO spike activity). The spatiotemporal firing dynamics of the experimental and simulation spike data were evaluated as feature vectors, including firing rates, local variation, auto-correlogram, cross-correlogram, and minimal distance, and were contracted onto two-dimensional principal component analysis (PCA) space. gc and gi were determined as the solution to the inverse problem such that the simulation and experimental spike data were closely matched in the PCA space. The goodness of the match was confirmed by an analysis of variance
Cao, Yongqiang; Grossberg, Stephen
2012-02-01
A laminar cortical model of stereopsis and 3D surface perception is developed and simulated. The model shows how spiking neurons that interact in hierarchically organized laminar circuits of the visual cortex can generate analog properties of 3D visual percepts. The model describes how monocular and binocular oriented filtering interact with later stages of 3D boundary formation and surface filling-in in the LGN and cortical areas V1, V2, and V4. It proposes how interactions between layers 4, 3B, and 2/3 in V1 and V2 contribute to stereopsis, and how binocular and monocular information combine to form 3D boundary and surface representations. The model suggests how surface-to-boundary feedback from V2 thin stripes to pale stripes helps to explain how computationally complementary boundary and surface formation properties lead to a single consistent percept, eliminate redundant 3D boundaries, and trigger figure-ground perception. The model also shows how false binocular boundary matches may be eliminated by Gestalt grouping properties. In particular, the disparity filter, which helps to solve the correspondence problem by eliminating false matches, is realized using inhibitory interneurons as part of the perceptual grouping process by horizontal connections in layer 2/3 of cortical area V2. The 3D sLAMINART model simulates 3D surface percepts that are consciously seen in 18 psychophysical experiments. These percepts include contrast variations of dichoptic masking and the correspondence problem, the effect of interocular contrast differences on stereoacuity, Panum's limiting case, the Venetian blind illusion, stereopsis with polarity-reversed stereograms, da Vinci stereopsis, and perceptual closure. The model hereby illustrates a general method of unlumping rate-based models that use the membrane equations of neurophysiology into models that use spiking neurons, and which may be embodied in VLSI chips that use spiking neurons to minimize heat production. Copyright
Methods for Estimating Neural Firing Rates, and Their Application to Brain-Machine Interfaces
Cunningham, John P.; Gilja, Vikash; Ryu, Stephen I.; Shenoy, Krishna V.
2009-01-01
Neural spike trains present analytical challenges due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of a spike train's underlying firing rate. Numerous methods for estimating neural firing rates have been developed in recent years, but to date no systematic comparison has been made between them. In this study, we review both classic and current firing rate estimation techniques. We compare the advantages and drawbacks of these methods. Then, in an effort to understand their relevance to the field of neural prostheses, we also apply these estimators to experimentally-gathered neural data from a prosthetic arm-reaching paradigm. Using these estimates of firing rate, we apply standard prosthetic decoding algorithms to compare the performance of the different firing rate estimators, and, perhaps surprisingly, we find minimal differences. This study serves as a review of available spike train smoothers and a first quantitative comparison of their performance for brain-machine interfaces. PMID:19349143
Mendoza-Poudereux, Isabel; Muñoz-Bertomeu, Jesús; Arrillaga, Isabel; Segura, Juan
2014-11-01
Spike lavender (Lavandula latifolia) is an economically important aromatic plant producing essential oils, whose components (mostly monoterpenes) are mainly synthesized through the plastidial methylerythritol 4-phosphate (MEP) pathway. 1-Deoxy-D-xylulose-5-phosphate (DXP) synthase (DXS), that catalyzes the first step of the MEP pathway, plays a crucial role in monoterpene precursors biosynthesis in spike lavender. To date, however, it is not known whether the DXP reductoisomerase (DXR), that catalyzes the conversion of DXP into MEP, is also a rate-limiting enzyme for the biosynthesis of monoterpenes in spike lavender. To investigate it, we generated transgenic spike lavender plants constitutively expressing the Arabidopsis thaliana DXR gene. Although two out of the seven transgenic T0 plants analyzed accumulated more essential oils than the controls, this is hardly imputable to the DXR transgene effect since a clear correlation between transcript accumulation and monoterpene production could not be established. Furthermore, these increased essential oil phenotypes were not maintained in their respective T1 progenies. Similar results were obtained when total chlorophyll and carotenoid content in both T0 transgenic plants and their progenies were analyzed. Our results then demonstrate that DXR enzyme does not play a crucial role in the synthesis of plastidial monoterpene precursors, suggesting that the control flux of the MEP pathway in spike lavender is primarily exerted by the DXS enzyme.
Baumann, Fabian; Obermayer, Klaus
2017-01-01
The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated. We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior of the underlying adaptive integrate-and-fire population. Particularly the cascade-based models are overall most accurate and robust, especially in the sensitive region of rapidly changing input. For the mean-driven regime, when input fluctuations are not too strong and fast, however, the best performing model is based on the spectral decomposition. The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback. The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants. Therefore we have made available implementations that allow to numerically integrate the low-dimensional spike rate models as well as the Fokker-Planck partial differential equation in efficient ways for
The Effects of Dynamical Synapses on Firing Rate Activity: A Spiking Neural Network Model.
Khalil, Radwa; Moftah, Marie Z; Moustafa, Ahmed A
2017-09-18
Accumulating evidence relates the fine-tuning of synaptic maturation and regulation of neural network activity to several key factors, including GABAA signaling and a lateral spread length between neighboring neurons (i.e. local connectivity). Furthermore, a number of studies consider Short-Term synaptic Plasticity (STP) as an essential element in the instant modification of synaptic efficacy in the neuronal network and in modulating responses to sustained ranges of external Poisson Input Frequency (IF). Nevertheless, evaluating the firing activity in response to the dynamical interaction between STP (triggered by ranges of IF), and these key parameters in vitro remains elusive. Therefore, we designed a Spiking Neural Network (SNN) model in which we incorporated the following parameters: local density of arbor essences and a lateral spread length between neighboring neurons. We also created several network scenarios based on these key parameters. Then, we implemented two classes of STP: (1) Short-Term synaptic Depression (STD), and (2) Short-Term synaptic Facilitation (STF). Each class has two differential forms based on the parametric value of its synaptic time constant (either for depressing or facilitating synapses). Lastly, we compared the neural firing responses before and after the treatment with STP. We found that dynamical synapses(STP) have a critical differential role on evaluating, and modulating the firing rate activity in each network scenario. Moreover, we investigated the impact of changing the balance between excitation (E) / inhibition (I) on stabilizing this firing activity. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Electromagnetic fields and EEG spiking rate in patients with focal epilepsy.
Curcio, Giuseppe; Mazzucchi, Edoardo; Della Marca, Giacomo; Vollono, Catello; Rossini, Paolo Maria
2015-04-01
Despite the increase in mobile telephone technology use and possible effects on brain excitability, no studies have investigated the impact of GSM like (Global System for Mobile Communications) signal on the ongoing spiking activity in human epileptic patients. Brain electrical (electroencephalogram, EEG) activity of 12 patients with focal epilepsy has been recorded under both Real and Sham exposure following a double-blind, crossover, counterbalanced design: before the exposure (pre-exposure/baseline session), during the Real or Sham 45 min exposure (during-exposure session), and after the exposure (post-exposure session). As dependent variables both spiking activity (spikes count) and EEG quantitative indices (spectral power and coherence data) have been considered. Spiking activity tended to be lower under Real than under Sham exposure. EEG spectral content analysis indicated a significant increase of Gamma band under Real exposure, mainly evident in Parieto-occipital and Temporal areas. Connectivity data indicated increased interhemispheric (left temporal to right frontal Regions of Interest, ROIs) instantaneous coherence, in the Beta frequency band during-exposure with respect to baseline session. No significant modification of lagged coherence was observed. Acute GSM exposure in epileptic patients slightly influences their EEG properties, without reaching any clinical relevance. No signs were found of an increased risk of incoming seizures for these patients as a consequence of using mobile phones. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Point process modeling and estimation: Advances in the analysis of dynamic neural spiking data
NASA Astrophysics Data System (ADS)
Deng, Xinyi
2016-08-01
A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in
Malaria transmission rates estimated from serological data.
Burattini, M. N.; Massad, E.; Coutinho, F. A.
1993-01-01
A mathematical model was used to estimate malaria transmission rates based on serological data. The model is minimally stochastic and assumes an age-dependent force of infection for malaria. The transmission rates estimated were applied to a simple compartmental model in order to mimic the malaria transmission. The model has shown a good retrieving capacity for serological and parasite prevalence data. PMID:8270011
Multiscale analysis of neural spike trains.
Ramezan, Reza; Marriott, Paul; Chenouri, Shojaeddin
2014-01-30
This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code.
Algorithm for symbol rate estimation of MFSK
NASA Astrophysics Data System (ADS)
Xu, Jian-fei; Wang, Fu-ping; Wang, Zanji
2011-10-01
Symbol rate is an important parameter in digital communication. In the area of non-cooperative communication, the precision of symbol rate estimation is essential to the success of subsequent signal processes such as demodulation. This paper proposes an approach for estimation of symbol rate for M-ary FSK signals. In this approach, an M-ary FSK signal is decomposed into M single tones, and the symbol rate is estimated by extracting the spectral line corresponding to the symbol rate in the spectrum of the summation of each single tone envelope signal. The implementation of the methodology does not require any apriori information. Computer simulations and analysis show that the approach provides high estimation accuracy and still keeps a good performance even at low input SNR.
Least squares estimation of avian molt rates
Johnson, D.H.
1989-01-01
A straightforward least squares method of estimating the rate at which birds molt feathers is presented, suitable for birds captured more than once during the period of molt. The date of molt onset can also be estimated. The method is applied to male and female mourning doves.
Convex weighting criteria for speaking rate estimation
Jiao, Yishan; Berisha, Visar; Tu, Ming; Liss, Julie
2015-01-01
Speaking rate estimation directly from the speech waveform is a long-standing problem in speech signal processing. In this paper, we pose the speaking rate estimation problem as that of estimating a temporal density function whose integral over a given interval yields the speaking rate within that interval. In contrast to many existing methods, we avoid the more difficult task of detecting individual phonemes within the speech signal and we avoid heuristics such as thresholding the temporal envelope to estimate the number of vowels. Rather, the proposed method aims to learn an optimal weighting function that can be directly applied to time-frequency features in a speech signal to yield a temporal density function. We propose two convex cost functions for learning the weighting functions and an adaptation strategy to customize the approach to a particular speaker using minimal training. The algorithms are evaluated on the TIMIT corpus, on a dysarthric speech corpus, and on the ICSI Switchboard spontaneous speech corpus. Results show that the proposed methods outperform three competing methods on both healthy and dysarthric speech. In addition, for spontaneous speech rate estimation, the result show a high correlation between the estimated speaking rate and ground truth values. PMID:26167516
Resting heart rate estimation using PIR sensors
NASA Astrophysics Data System (ADS)
Kapu, Hemanth; Saraswat, Kavisha; Ozturk, Yusuf; Cetin, A. Enis
2017-09-01
In this paper, we describe a non-invasive and non-contact system of estimating resting heart rate (RHR) using a pyroelectric infrared (PIR) sensor. This infrared system monitors and records the chest motion of a subject using the analog output signal of the PIR sensor. The analog output signal represents the composite motion due to inhale-exhale process with magnitude much larger than the minute vibrations of heartbeat. Since the acceleration of the heart activity is much faster than breathing the second derivative of the PIR sensor signal monitoring the chest of the subject is used to estimate the resting heart rate. Experimental results indicate that this ambient sensor can measure resting heart rate with a chi-square significance level of α = 0.05 compared to an industry standard PPG sensor. This new system provides a low cost and an effective way to estimate the resting heart rate, which is an important biological marker.
Oñativia, Jon; Schultz, Simon R; Dragotti, Pier Luigi
2014-01-01
Objective Inferring the times of sequences of action potentials (APs) (spike trains) from neurophysiological data is a key problem in computational neuroscience. The detection of APs from two-photon imaging of calcium signals offers certain advantages over traditional electrophysiological approaches, as up to thousands of spatially and immunohistochemically defined neurons can be recorded simultaneously. However, due to noise, dye buffering and the limited sampling rates in common microscopy configurations, accurate detection of APs from calcium time series has proved to be a difficult problem. Approach Here we introduce a novel approach to the problem making use of finite rate of innovation (FRI) theory (Vetterli et al 2002 IEEE Trans. Signal Process. 50 1417–28). For calcium transients well fit by a single exponential, the problem is reduced to reconstructing a stream of decaying exponentials. Signals made of a combination of exponentially decaying functions with different onset times are a subclass of FRI signals, for which much theory has recently been developed by the signal processing community. Main results We demonstrate for the first time the use of FRI theory to retrieve the timing of APs from calcium transient time series. The final algorithm is fast, non-iterative and parallelizable. Spike inference can be performed in real-time for a population of neurons and does not require any training phase or learning to initialize parameters. Significance The algorithm has been tested with both real data (obtained by simultaneous electrophysiology and multiphoton imaging of calcium signals in cerebellar Purkinje cell dendrites), and surrogate data, and outperforms several recently proposed methods for spike train inference from calcium imaging data. PMID:23860257
NASA Astrophysics Data System (ADS)
Oñativia, Jon; Schultz, Simon R.; Dragotti, Pier Luigi
2013-08-01
Objective. Inferring the times of sequences of action potentials (APs) (spike trains) from neurophysiological data is a key problem in computational neuroscience. The detection of APs from two-photon imaging of calcium signals offers certain advantages over traditional electrophysiological approaches, as up to thousands of spatially and immunohistochemically defined neurons can be recorded simultaneously. However, due to noise, dye buffering and the limited sampling rates in common microscopy configurations, accurate detection of APs from calcium time series has proved to be a difficult problem. Approach. Here we introduce a novel approach to the problem making use of finite rate of innovation (FRI) theory (Vetterli et al 2002 IEEE Trans. Signal Process. 50 1417-28). For calcium transients well fit by a single exponential, the problem is reduced to reconstructing a stream of decaying exponentials. Signals made of a combination of exponentially decaying functions with different onset times are a subclass of FRI signals, for which much theory has recently been developed by the signal processing community. Main results. We demonstrate for the first time the use of FRI theory to retrieve the timing of APs from calcium transient time series. The final algorithm is fast, non-iterative and parallelizable. Spike inference can be performed in real-time for a population of neurons and does not require any training phase or learning to initialize parameters. Significance. The algorithm has been tested with both real data (obtained by simultaneous electrophysiology and multiphoton imaging of calcium signals in cerebellar Purkinje cell dendrites), and surrogate data, and outperforms several recently proposed methods for spike train inference from calcium imaging data.
Bayes Error Rate Estimation Using Classifier Ensembles
NASA Technical Reports Server (NTRS)
Tumer, Kagan; Ghosh, Joydeep
2003-01-01
The Bayes error rate gives a statistical lower bound on the error achievable for a given classification problem and the associated choice of features. By reliably estimating th is rate, one can assess the usefulness of the feature set that is being used for classification. Moreover, by comparing the accuracy achieved by a given classifier with the Bayes rate, one can quantify how effective that classifier is. Classical approaches for estimating or finding bounds for the Bayes error, in general, yield rather weak results for small sample sizes; unless the problem has some simple characteristics, such as Gaussian class-conditional likelihoods. This article shows how the outputs of a classifier ensemble can be used to provide reliable and easily obtainable estimates of the Bayes error with negligible extra computation. Three methods of varying sophistication are described. First, we present a framework that estimates the Bayes error when multiple classifiers, each providing an estimate of the a posteriori class probabilities, a recombined through averaging. Second, we bolster this approach by adding an information theoretic measure of output correlation to the estimate. Finally, we discuss a more general method that just looks at the class labels indicated by ensem ble members and provides error estimates based on the disagreements among classifiers. The methods are illustrated for artificial data, a difficult four-class problem involving underwater acoustic data, and two problems from the Problem benchmarks. For data sets with known Bayes error, the combiner-based methods introduced in this article outperform existing methods. The estimates obtained by the proposed methods also seem quite reliable for the real-life data sets for which the true Bayes rates are unknown.
Multiparameter respiratory rate estimation from the photoplethysmogram.
Karlen, Walter; Raman, Srinivas; Ansermino, J Mark; Dumont, Guy A
2013-07-01
We present a novel method for estimating respiratory rate in real time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory-induced variation is analyzed using fast Fourier transforms. The proposed Smart Fusion method then combines the results of the three respiratory-induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory-induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2, and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.
Improved entropy rate estimation in physiological data.
Lake, D E
2011-01-01
Calculating entropy rate in physiologic signals has proven very useful in many settings. Common entropy estimates for this purpose are sample entropy (SampEn) and its less robust elder cousin, approximate entropy (ApEn). Both approaches count matches within a tolerance r for templates of length m consecutive observations. When physiologic data records are long and well-behaved, both approaches work very well for a wide range of m and r. However, more attention to the details of the estimation algorithm is needed for short records and signals with anomalies. In addition, interpretation of the magnitude of these estimates is highly dependent on how r is chosen and precludes comparison across studies with even slightly different methodologies. In this paper, we summarize recent novel approaches to improve the accuracy of entropy estimation. An important (but not necessarily new) alternative to current approaches is to develop estimates that convert probabilities to densities by normalizing by the matching region volume. This approach leads to a novel concept introduced here of reporting entropy rate in equivalent Gaussian white noise units. Another approach is to allow r to vary so that a pre-specified number of matches are found, called the minimum numerator count, to ensure confident probability estimation. The approaches are illustrated using a simple example of detecting abnormal cardiac rhythms in heart rate records.
Simple estimate of the human metabolic rate
NASA Astrophysics Data System (ADS)
Graham, Daniel J.; Schacht, David V.
2001-06-01
A method for estimating the human metabolic rate is described. It entails measuring the rate at which carbon dioxide is produced by glucose oxidation during respiration. Such measurements can enhance classroom presentations of the concept of energy and its interconversion. Measurements of this type can also augment classroom discussions of related topics such as entropy production in nonequilibrium systems. The ideas are appropriate at both the high school and college levels and should appeal to student interest in metabolism, physiology, and medical physics.
Monitoring spike train synchrony.
Kreuz, Thomas; Chicharro, Daniel; Houghton, Conor; Andrzejak, Ralph G; Mormann, Florian
2013-03-01
Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.
Estimated recharge rates at the Hanford Site
Fayer, M.J.; Walters, T.B.
1995-02-01
The Ground-Water Surveillance Project monitors the distribution of contaminants in ground water at the Hanford Site for the U.S. Department of Energy. A subtask called {open_quotes}Water Budget at Hanford{close_quotes} was initiated in FY 1994. The objective of this subtask was to produce a defensible map of estimated recharge rates across the Hanford Site. Methods that have been used to estimate recharge rates at the Hanford Site include measurements (of drainage, water contents, and tracers) and computer modeling. For the simulations of 12 soil-vegetation combinations, the annual rates varied from 0.05 mm/yr for the Ephrata sandy loam with bunchgrass to 85.2 mm/yr for the same soil without vegetation. Water content data from the Grass Site in the 300 Area indicated that annual rates varied from 3.0 to 143.5 mm/yr during an 8-year period. The annual volume of estimated recharge was calculated to be 8.47 {times} 10{sup 9} L for the potential future Hanford Site (i.e., the portion of the current Site bounded by Highway 240 and the Columbia River). This total volume is similar to earlier estimates of natural recharge and is 2 to 10x higher than estimates of runoff and ground-water flow from higher elevations. Not only is the volume of natural recharge significant in comparison to other ground-water inputs, the distribution of estimated recharge is highly skewed to the disturbed sandy soils (i.e., the 200 Areas, where most contaminants originate). The lack of good estimates of the means and variances of the supporting data (i.e., the soil map, the vegetation/land use map, the model parameters) translates into large uncertainties in the recharge estimates. When combined, the significant quantity of estimated recharge, its high sensitivity to disturbance, and the unquantified uncertainty of the data and model parameters suggest that the defensibility of the recharge estimates should be improved.
Satellite Angular Rate Estimation From Vector Measurements
NASA Technical Reports Server (NTRS)
Azor, Ruth; Bar-Itzhack, Itzhack Y.; Harman, Richard R.
1996-01-01
This paper presents an algorithm for estimating the angular rate vector of a satellite which is based on the time derivatives of vector measurements expressed in a reference and body coordinate. The computed derivatives are fed into a spacial Kalman filter which yields an estimate of the spacecraft angular velocity. The filter, named Extended Interlaced Kalman Filter (EIKF), is an extension of the Kalman filter which, although being linear, estimates the state of a nonlinear dynamic system. It consists of two or three parallel Kalman filters whose individual estimates are fed to one another and are considered as known inputs by the other parallel filter(s). The nonlinear dynamics stem from the nonlinear differential equation that describes the rotation of a three dimensional body. Initial results, using simulated data, and real Rossi X ray Timing Explorer (RXTE) data indicate that the algorithm is efficient and robust.
Estimating recharge rates with analytic element models and parameter estimation
Dripps, W.R.; Hunt, R.J.; Anderson, M.P.
2006-01-01
Quantifying the spatial and temporal distribution of recharge is usually a prerequisite for effective ground water flow modeling. In this study, an analytic element (AE) code (GFLOW) was used with a nonlinear parameter estimation code (UCODE) to quantify the spatial and temporal distribution of recharge using measured base flows as calibration targets. The ease and flexibility of AE model construction and evaluation make this approach well suited for recharge estimation. An AE flow model of an undeveloped watershed in northern Wisconsin was optimized to match median annual base flows at four stream gages for 1996 to 2000 to demonstrate the approach. Initial optimizations that assumed a constant distributed recharge rate provided good matches (within 5%) to most of the annual base flow estimates, but discrepancies of >12% at certain gages suggested that a single value of recharge for the entire watershed is inappropriate. Subsequent optimizations that allowed for spatially distributed recharge zones based on the distribution of vegetation types improved the fit and confirmed that vegetation can influence spatial recharge variability in this watershed. Temporally, the annual recharge values varied >2.5-fold between 1996 and 2000 during which there was an observed 1.7-fold difference in annual precipitation, underscoring the influence of nonclimatic factors on interannual recharge variability for regional flow modeling. The final recharge values compared favorably with more labor-intensive field measurements of recharge and results from studies, supporting the utility of using linked AE-parameter estimation codes for recharge estimation. Copyright ?? 2005 The Author(s).
Yi, Guo-Sheng; Wang, Jiang; Tsang, Kai-Ming; Wei, Xi-Le; Deng, Bin
2015-01-01
Dynamic spike threshold plays a critical role in neuronal input-output relations. In many neurons, the threshold potential depends on the rate of membrane potential depolarization (dV/dt) preceding a spike. There are two basic classes of neural excitability, i.e., Type I and Type II, according to input-output properties. Although the dynamical and biophysical basis of their spike initiation has been established, the spike threshold dynamic for each cell type has not been well described. Here, we use a biophysical model to investigate how spike threshold depends on dV/dt in two types of neuron. It is observed that Type II spike threshold is more depolarized and more sensitive to dV/dt than Type I. With phase plane analysis, we show that each threshold dynamic arises from the different separatrix and K+ current kinetics. By analyzing subthreshold properties of membrane currents, we find the activation of hyperpolarizing current prior to spike initiation is a major factor that regulates the threshold dynamics. The outward K+ current in Type I neuron does not activate at the perithresholds, which makes its spike threshold insensitive to dV/dt. The Type II K+ current activates prior to spike initiation and there is a large net hyperpolarizing current at the perithresholds, which results in a depolarized threshold as well as a pronounced threshold dynamic. These predictions are further attested in several other functionally equivalent cases of neural excitability. Our study provides a fundamental description about how intrinsic biophysical properties contribute to the threshold dynamics in Type I and Type II neurons, which could decipher their significant functions in neural coding. PMID:26083350
Estimating Virus Production Rates in Aquatic Systems
Matteson, Audrey R.; Budinoff, Charles R.; Campbell, Claire E.; Buchan, Alison; Wilhelm, Steven W.
2010-01-01
Viruses are pervasive components of marine and freshwater systems, and are known to be significant agents of microbial mortality. Developing quantitative estimates of this process is critical as we can then develop better models of microbial community structure and function as well as advance our understanding of how viruses work to alter aquatic biogeochemical cycles. The virus reduction technique allows researchers to estimate the rate at which virus particles are released from the endemic microbial community. In brief, the abundance of free (extracellular) viruses is reduced in a sample while the microbial community is maintained at near ambient concentration. The microbial community is then incubated in the absence of free viruses and the rate at which viruses reoccur in the sample (through the lysis of already infected members of the community) can be quantified by epifluorescence microscopy or, in the case of specific viruses, quantitative PCR. These rates can then be used to estimate the rate of microbial mortality due to virus-mediated cell lysis. PMID:20972392
Baker, R.J.; Baehr, A.L.; Lahvis, M.A.
2000-01-01
An open microcosm method for quantifying microbial respiration and estimating biodegradation rates of hydrocarbons in gasoline-contaminated sediment samples has been developed and validated. Stainless-steel bioreactors are filled with soil or sediment samples, and the vapor-phase composition (concentrations of oxygen (O2), nitrogen (N2), carbon dioxide (CO2), and selected hydrocarbons) is monitored over time. Replacement gas is added as the vapor sample is taken, and selection of the replacement gas composition facilitates real-time decision-making regarding environmental conditions within the bioreactor. This capability allows for maintenance of field conditions over time, which is not possible in closed microcosms. Reaction rates of CO2 and O2 are calculated from the vapor-phase composition time series. Rates of hydrocarbon biodegradation are either measured directly from the hydrocarbon mass balance, or estimated from CO2 and O2 reaction rates and assumed reaction stoichiometries. Open microcosm experiments using sediments spiked with toluene and p-xylene were conducted to validate the stoichiometric assumptions. Respiration rates calculated from O2 consumption and from CO2 production provide estimates of toluene and p- xylene degradation rates within about ??50% of measured values when complete mineralization stoichiometry is assumed. Measured values ranged from 851.1 to 965.1 g m-3 year-1 for toluene, and 407.2-942.3 g m-3 year-1 for p- xylene. Contaminated sediment samples from a gasoline-spill site were used in a second set of microcosm experiments. Here, reaction rates of O2 and CO2 were measured and used to estimate hydrocarbon respiration rates. Total hydrocarbon reaction rates ranged from 49.0 g m-3 year-1 in uncontaminated (background) to 1040.4 g m-3 year-1 for highly contaminated sediment, based on CO2 production data. These rate estimates were similar to those obtained independently from in situ CO2 vertical gradient and flux determinations at the
NASA Astrophysics Data System (ADS)
Baker, Ronald J.; Baehr, Arthur L.; Lahvis, Matthew A.
2000-01-01
An open microcosm method for quantifying microbial respiration and estimating biodegradation rates of hydrocarbons in gasoline-contaminated sediment samples has been developed and validated. Stainless-steel bioreactors are filled with soil or sediment samples, and the vapor-phase composition (concentrations of oxygen (O 2), nitrogen (N 2), carbon dioxide (CO 2), and selected hydrocarbons) is monitored over time. Replacement gas is added as the vapor sample is taken, and selection of the replacement gas composition facilitates real-time decision-making regarding environmental conditions within the bioreactor. This capability allows for maintenance of field conditions over time, which is not possible in closed microcosms. Reaction rates of CO 2 and O 2 are calculated from the vapor-phase composition time series. Rates of hydrocarbon biodegradation are either measured directly from the hydrocarbon mass balance, or estimated from CO 2 and O 2 reaction rates and assumed reaction stoichiometries. Open microcosm experiments using sediments spiked with toluene and p-xylene were conducted to validate the stoichiometric assumptions. Respiration rates calculated from O 2 consumption and from CO 2 production provide estimates of toluene and p-xylene degradation rates within about ±50% of measured values when complete mineralization stoichiometry is assumed. Measured values ranged from 851.1 to 965.1 g m -3 year -1 for toluene, and 407.2-942.3 g m -3 year -1 for p-xylene. Contaminated sediment samples from a gasoline-spill site were used in a second set of microcosm experiments. Here, reaction rates of O 2 and CO 2 were measured and used to estimate hydrocarbon respiration rates. Total hydrocarbon reaction rates ranged from 49.0 g m -3 year -1 in uncontaminated (background) to 1040.4 g m -3 year -1 for highly contaminated sediment, based on CO 2 production data. These rate estimates were similar to those obtained independently from in situ CO 2 vertical gradient and flux
Towards universal hybrid star formation rate estimators
NASA Astrophysics Data System (ADS)
Boquien, M.; Kennicutt, R.; Calzetti, D.; Dale, D.; Galametz, M.; Sauvage, M.; Croxall, K.; Draine, B.; Kirkpatrick, A.; Kumari, N.; Hunt, L.; De Looze, I.; Pellegrini, E.; Relaño, M.; Smith, J.-D.; Tabatabaei, F.
2016-06-01
Context. To compute the star formation rate (SFR) of galaxies from the rest-frame ultraviolet (UV), it is essential to take the obscuration by dust into account. To do so, one of the most popular methods consists in combining the UV with the emission from the dust itself in the infrared (IR). Yet, different studies have derived different estimators, showing that no such hybrid estimator is truly universal. Aims: In this paper we aim at understanding and quantifying what physical processes fundamentally drive the variations between different hybrid estimators. In so doing, we aim at deriving new universal UV+IR hybrid estimators to correct the UV for dust attenuation at local and global scales, taking the intrinsic physical properties of galaxies into account. Methods: We use the CIGALE code to model the spatially resolved far-UV to far-IR spectral energy distributions of eight nearby star-forming galaxies drawn from the KINGFISH sample. This allows us to determine their local physical properties, and in particular their UV attenuation, average SFR, average specific SFR (sSFR), and their stellar mass. We then examine how hybrid estimators depend on said properties. Results: We find that hybrid UV+IR estimators strongly depend on the stellar mass surface density (in particular at 70 μm and 100 μm) and on the sSFR (in particular at 24 μm and the total infrared). Consequently, the IR scaling coefficients for UV obscuration can vary by almost an order of magnitude: from 1.55 to 13.45 at 24 μm for instance. This result contrasts with other groups who found relatively constant coefficients with small deviations. We exploit these variations to construct a new class of adaptative hybrid estimators based on observed UV to near-IR colours and near-IR luminosity densities per unit area. We find that they can reliably be extended to entire galaxies. Conclusions: The new estimators provide better estimates of attenuation-corrected UV emission than classical hybrid estimators
Estimates of EPSP amplitude based on changes in motoneuron discharge rate and probability.
Powers, Randall K; Türker, K S
2010-10-01
When motor units are discharging tonically, transient excitatory synaptic inputs produce an increase in the probability of spike occurrence and also increase the instantaneous discharge rate. Several researchers have proposed that these induced changes in discharge rate and probability can be used to estimate the amplitude of the underlying excitatory post-synaptic potential (EPSP). We tested two different methods of estimating EPSP amplitude by comparing the amplitude of simulated EPSPs with their effects on the discharge of rat hypoglossal motoneurons recorded in an in vitro brainstem slice preparation. The first estimation method (simplified-trajectory method) is based on the assumptions that the membrane potential trajectory between spikes can be approximated by a 10 mV post-spike hyperpolarization followed by a linear rise to the next spike and that EPSPs sum linearly with this trajectory. We hypothesized that this estimation method would not be accurate due to interspike variations in membrane conductance and firing threshold that are not included in the model and that an alternative method based on estimating the effective distance to threshold would provide more accurate estimates of EPSP amplitude. This second method (distance-to-threshold method) uses interspike interval statistics to estimate the effective distance to threshold throughout the interspike interval and incorporates this distance-to-threshold trajectory into a threshold-crossing model. We found that the first method systematically overestimated the amplitude of small (<5 mV) EPSPs and underestimated the amplitude of large (>5 mV EPSPs). For large EPSPs, the degree of underestimation increased with increasing background discharge rate. Estimates based on the second method were more accurate for small EPSPs than those based on the first model, but estimation errors were still large for large EPSPs. These errors were likely due to two factors: (1) the distance to threshold can only be
Estimating instantaneous respiratory rate from the photoplethysmogram.
Dehkordi, Parastoo; Garde, Ainara; Molavi, Behnam; Petersen, Christian L; Ansermino, J Mark; Dumont, Guy A
2015-01-01
The photoplethysmogram (PPG) obtained from pulse oximetry shows the local changes of blood volume in tissues. Respiration induces variation in the PPG baseline due to the variation in venous blood return during each breathing cycle. We have proposed an algorithm based on the synchrosqueezing transform (SST) to estimate instantaneous respiratory rate (IRR) from the PPG. The SST is a combination of wavelet analysis and a reallocation method which aims to sharpen the time-frequency representation of the signal and can provide an accurate estimation of instantaneous frequency. In this application, the SST was applied to the PPG and IRR was detected as the predominant ridge in the respiratory band (0.1 Hz - 1 Hz) in the SST plane. The algorithm was tested against the Capnobase benchmark dataset that contains PPG, capnography, and expert labelled reference respiratory rate from 42 subjects. The IRR estimation accuracy was assessed using the root mean square (RMS) error and Bland-Altman plot. The median RMS error was 0.39 breaths/min for all subjects which ranged from the lowest error of 0.18 breaths/min to the highest error of 13.86 breaths/min. A Bland-Altman plot showed an agreement between the IRR obtained from PPG and reference respiratory rate with a bias of -0.32 and limits agreement of -7.72 to 7.07. Extracting IRR from PPG expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.
Grammont, F; Riehle, A
2003-05-01
We studied the dynamics of precise spike synchronization and rate modulation in a population of neurons recorded in monkey motor cortex during performance of a delayed multidirectional pointing task and determined their relation to behavior. We showed that at the population level neurons coherently synchronized their activity at various moments during the trial in relation to relevant task events. The comparison of the time course of the modulation of synchronous activity with that of the firing rate of the same neurons revealed a considerable difference. Indeed, when synchronous activity was highest, at the end of the preparatory period, firing rate was low, and, conversely, when the firing rate was highest, at movement onset, synchronous activity was almost absent. There was a clear tendency for synchrony to precede firing rate, suggesting that the coherent activation of cell assemblies may trigger the increase in firing rate in large groups of neurons, although it appeared that there was no simple parallel shifting in time of these two activity measures. Interestingly, there was a systematic relationship between the amount of significant synchronous activity within the population of neurons and movement direction at the end of the preparatory period. Furthermore, about 400 ms later, at movement onset, the mean firing rate of the same population was also significantly tuned to movement direction, having roughly the same preferred direction as synchronous activity. Finally, reaction time measurements revealed a directional preference of the monkey with, once again, the same preferred direction as synchronous activity and firing rate. These results lead us to speculate that synchronous activity and firing rate are cooperative neuronal processes and that the directional matching of our three measures--firing rate, synchronicity, and reaction times--might be an effect of behaviorally induced network cooperativity acquired during learning.
Estimation of hydrolysis rate constants for carbamates ...
Cheminformatics based tools, such as the Chemical Transformation Simulator under development in EPA’s Office of Research and Development, are being increasingly used to evaluate chemicals for their potential to degrade in the environment or be transformed through metabolism. Hydrolysis represents a major environmental degradation pathway; unfortunately, only a small fraction of hydrolysis rates for about 85,000 chemicals on the Toxic Substances Control Act (TSCA) inventory are in public domain, making it critical to develop in silico approaches to estimate hydrolysis rate constants. In this presentation, we compare three complementary approaches to estimate hydrolysis rates for carbamates, an important chemical class widely used in agriculture as pesticides, herbicides and fungicides. Fragment-based Quantitative Structure Activity Relationships (QSARs) using Hammett-Taft sigma constants are widely published and implemented for relatively simple functional groups such as carboxylic acid esters, phthalate esters, and organophosphate esters, and we extend these to carbamates. We also develop a pKa based model and a quantitative structure property relationship (QSPR) model, and evaluate them against measured rate constants using R square and root mean square (RMS) error. Our work shows that for our relatively small sample size of carbamates, a Hammett-Taft based fragment model performs best, followed by a pKa and a QSPR model. This presentation compares three comp
Consensus-Based Sorting of Neuronal Spike Waveforms
Fournier, Julien; Mueller, Christian M.; Shein-Idelson, Mark; Hemberger, Mike
2016-01-01
Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data. PMID:27536990
Bias in Estimation of Misclassification Rates.
Haberman, Shelby J
2006-06-01
When a simple random sample of size n is employed to establish a classification rule for prediction of a polytomous variable by an independent variable, the best achievable rate of misclassification is higher than the corresponding best achievable rate if the conditional probability distribution is known for the predicted variable given the independent variable. In typical cases, this increased misclassification rate due to sampling is remarkably small relative to other increases in expected measures of prediction accuracy due to samplings that are typically encountered in statistical analysis.This issue is particularly striking if a polytomous variable predicts a polytomous variable, for the excess misclassification rate due to estimation approaches 0 at an exponential rate as n increases. Even with a continuous real predictor and with simple nonparametric methods, it is typically not difficult to achieve an excess misclassification rate on the order of n (-1). Although reduced excess error is normally desirable, it may reasonably be argued that, in the case of classification, the reduction in bias is related to a more fundamental lack of sensitivity of misclassification error to the quality of the prediction. This lack of sensitivity is not an issue if criteria based on probability prediction such as logarithmic penalty or least squares are employed, but the latter measures typically involve more substantial issues of bias. With polytomous predictors, excess expected errors due to sampling are typically of order n (-1). For a continuous real predictor, the increase in expected error is typically of order n (-2/3).
Quantitative Estimation of Tissue Blood Flow Rate.
Tozer, Gillian M; Prise, Vivien E; Cunningham, Vincent J
2016-01-01
The rate of blood flow through a tissue (F) is a critical parameter for assessing the functional efficiency of a blood vessel network following angiogenesis. This chapter aims to provide the principles behind the estimation of F, how F relates to other commonly used measures of tissue perfusion, and a practical approach for estimating F in laboratory animals, using small readily diffusible and metabolically inert radio-tracers. The methods described require relatively nonspecialized equipment. However, the analytical descriptions apply equally to complementary techniques involving more sophisticated noninvasive imaging.Two techniques are described for the quantitative estimation of F based on measuring the rate of tissue uptake following intravenous administration of radioactive iodo-antipyrine (or other suitable tracer). The Tissue Equilibration Technique is the classical approach and the Indicator Fractionation Technique, which is simpler to perform, is a practical alternative in many cases. The experimental procedures and analytical methods for both techniques are given, as well as guidelines for choosing the most appropriate method.
Revisiting the Estimation of Dinosaur Growth Rates
Myhrvold, Nathan P.
2013-01-01
Previous growth-rate studies covering 14 dinosaur taxa, as represented by 31 data sets, are critically examined and reanalyzed by using improved statistical techniques. The examination reveals that some previously reported results cannot be replicated by using the methods originally reported; results from new methods are in many cases different, in both the quantitative rates and the qualitative nature of the growth, from results in the prior literature. Asymptotic growth curves, which have been hypothesized to be ubiquitous, are shown to provide best fits for only four of the 14 taxa. Possible reasons for non-asymptotic growth patterns are discussed; they include systematic errors in the age-estimation process and, more likely, a bias toward younger ages among the specimens analyzed. Analysis of the data sets finds that only three taxa include specimens that could be considered skeletally mature (i.e., having attained 90% of maximum body size predicted by asymptotic curve fits), and eleven taxa are quite immature, with the largest specimen having attained less than 62% of predicted asymptotic size. The three taxa that include skeletally mature specimens are included in the four taxa that are best fit by asymptotic curves. The totality of results presented here suggests that previous estimates of both maximum dinosaur growth rates and maximum dinosaur sizes have little statistical support. Suggestions for future research are presented. PMID:24358133
Conwit, R A; Tracy, B; Cowl, A; McHugh, M; Stashuk, D; Brown, W F; Metter, E J
1998-10-01
Electromyographic signals detected from the quadriceps femoris during various constant force contractions were decomposed to identify individual motor unit discharges and mean firing rates (FRs). Subject and group mean FRs were calculated for each force level. Mean FR values and FR variability increased with force. Individual, subject, and group mean FRs showed slight increases until 30% of maximum voluntary contraction and larger increases thereafter. Findings are discussed in relation to motor unit recruitment, frequency modulation, and fatigue.
Estimating diversification rates from phylogenetic information.
Ricklefs, Robert E
2007-11-01
Patterns of species richness reflect the balance between speciation and extinction over the evolutionary history of life. These processes are influenced by the size and geographical complexity of regions, conditions of the environment, and attributes of individuals and species. Diversity within clades also depends on age and thus the time available for accumulating species. Estimating rates of diversification is key to understanding how these factors have shaped patterns of species richness. Several approaches to calculating both relative and absolute rates of speciation and extinction within clades are based on phylogenetic reconstructions of evolutionary relationships. As the size and quality of phylogenies increases, these approaches will find broader application. However, phylogeny reconstruction fosters a perceptual bias of continual increase in species richness, and the analysis of primarily large clades produces a data selection bias. Recognizing these biases will encourage the development of more realistic models of diversification and the regulation of species richness.
Estimation of Europa's exosphere loss rates
NASA Astrophysics Data System (ADS)
Lucchetti, Alice; Plainaki, Christina; Cremonese, Gabriele; Milillo, Anna; Shematovich, Valery; Jia, Xianzhe; Cassidy, Timothy
2015-04-01
Reactions in Europa's exosphere are dominated by plasma interactions with neutrals. The cross-sections for these processes are energy dependent and therefore the respective loss rates of the exospheric species depend on the speed distribution of the charged particles relative to the neutrals, as well as the densities of each reactant. In this work we review the average H2O, O2, and H2 loss rates due to plasma-neutral interactions to perform an estimation of the Europa's total exosphere loss. Since the electron density at Europa's orbit varies significantly with the magnetic latitude of the moon in Jupiter's magnetosphere, the dissociation and ionization rates for electron-impact processes are subject to spatial and temporal variations. Therefore, the resulting neutral loss rates determining the actual spatial distribution of the neutral density is not homogeneous. In addition, the ion-neutral interactions have an input to the loss of exospheric species as well as to the modification of the energy distribution of the existing species (for example, the O2 energy distribution is modified through charge-exchange between O2 and O2+). In our calculations, the photoreactions were considered for conditions of quiet and active Sun.
Robust Speech Rate Estimation for Spontaneous Speech
Wang, Dagen; Narayanan, Shrikanth S.
2010-01-01
In this paper, we propose a direct method for speech rate estimation from acoustic features without requiring any automatic speech transcription. We compare various spectral and temporal signal analysis and smoothing strategies to better characterize the underlying syllable structure to derive speech rate. The proposed algorithm extends the methods of spectral subband correlation by including temporal correlation and the use of prominent spectral subbands for improving the signal correlation essential for syllable detection. Furthermore, to address some of the practical robustness issues in previously proposed methods, we introduce some novel components into the algorithm such as the use of pitch confidence for filtering spurious syllable envelope peaks, magnifying window for tackling neighboring syllable smearing, and relative peak measure thresholds for pseudo peak rejection. We also describe an automated approach for learning algorithm parameters from data, and find the optimal settings through Monte Carlo simulations and parameter sensitivity analysis. Final experimental evaluations are conducted based on a portion of the Switchboard corpus for which manual phonetic segmentation information, and published results for direct comparison are available. The results show a correlation coefficient of 0.745 with respect to the ground truth based on manual segmentation. This result is about a 17% improvement compared to the current best single estimator and a 11% improvement over the multiestimator evaluated on the same Switchboard database. PMID:20428476
Bayesian Estimation of Thermonuclear Reaction Rates
NASA Astrophysics Data System (ADS)
Iliadis, C.; Anderson, K. S.; Coc, A.; Timmes, F. X.; Starrfield, S.
2016-11-01
The problem of estimating non-resonant astrophysical S-factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We present astrophysical S-factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p,γ)3He, 3He(3He,2p)4He, and 3He(α,γ)7Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.
Estimation of metal strength at very high rates using free-surface Richtmyer–Meshkov Instabilities
Prime, Michael Bruce; Buttler, William Tillman; Buechler, Miles Allen; ...
2017-03-08
Recently, Richtmyer–Meshkov Instabilities (RMI) have been proposed for studying the average strength at strain rates up to at least 107/s. RMI experiments involve shocking a metal interface that has initial sinusoidal perturbations. The perturbations invert and grow subsequent to shock and may arrest because of strength effects. In this work we present new RMI experiments and data on a copper target that had five regions with different perturbation amplitudes on the free surface opposite the shock. We estimate the high-rate, low-pressure copper strength by comparing experimental data with Lagrangian numerical simulations. From a detailed computational study we find that meshmore » convergence must be carefully addressed to accurately compare with experiments, and numerical viscosity has a strong influence on convergence. We also find that modeling the as-built perturbation geometry rather than the nominal makes a significant difference. Because of the confounding effect of tensile damage on total spike growth, which has previously been used as the metric for estimating strength, we instead use a new strength metric: the peak velocity during spike growth. Furthermore, this new metric also allows us to analyze a broader set of experimental results that are sensitive to strength because some larger initial perturbations grow unstably to failure and so do not have a finite total spike growth.« less
A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings
Chichilnisky, E. J.; Simoncelli, Eero P.
2013-01-01
We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call “binary pursuit”. The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth. PMID:23671583
A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.
Pillow, Jonathan W; Shlens, Jonathon; Chichilnisky, E J; Simoncelli, Eero P
2013-01-01
We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.
Hashemi, M; Valizadeh, A; Azizi, Y
2012-02-01
A recurrent loop consisting of a single Hodgkin-Huxley neuron influenced by a chemical excitatory delayed synaptic feedback is considered. We show that the behavior of the system depends on the duration of the activity of the synapse, which is determined by the activation and deactivation time constants of the synapse. For the fast synapses, those for which the effect of the synaptic activity is small compared to the period of firing, depending on the delay time, spiking with single and multiple interspike intervals is possible and the average firing rate can be smaller or larger than that of the open loop neuron. For slow synapses for which the synaptic time constants are of order of the period of the firing, the self-excitation increases the firing rate for all values of the delay time. We also show that for a chain consisting of few similar oscillators, if the synapses are chosen from different time constants, the system will follow the dynamics imposed by the fastest element, which is the oscillator that receives excitations via a slow synapse. The generalization of the results to other types of relaxation oscillators is discussed and the results are compared to those of the loops with inhibitory synapses as well as with gap junctions. © 2012 American Physical Society
Accidental Turbulent Discharge Rate Estimation from Videos
NASA Astrophysics Data System (ADS)
Ibarra, Eric; Shaffer, Franklin; Savaş, Ömer
2015-11-01
A technique to estimate the volumetric discharge rate in accidental oil releases using high speed video streams is described. The essence of the method is similar to PIV processing, however the cross correlation is carried out on the visible features of the efflux, which are usually turbulent, opaque and immiscible. The key step in the process is to perform a pixelwise time filtering on the video stream, in which the parameters are commensurate with the scales of the large eddies. The velocity field extracted from the shell of visible features is then used to construct an approximate velocity profile within the discharge. The technique has been tested on laboratory experiments using both water and oil jets at Re ~105 . The technique is accurate to 20%, which is sufficient for initial responders to deploy adequate resources for containment. The software package requires minimal user input and is intended for deployment on an ROV in the field. Supported by DOI via NETL.
Kim, Bojeong; Kim, Young Sik; Kim, Bo Min; Hay, Anthony G; McBride, Murray B
2011-03-01
A systematic investigation into lowered degradation rates of glyphosate in metal-contaminated soils was performed by measuring mineralization of [(14)C]glyphosate to (14)CO(2) in two mineral soils that had been spiked with Cu and/or Zn at various loadings. Cumulative (14)CO(2) release was estimated to be approximately 6% or less of the amount of [(14)C]glyphosate originally added in both soils over an 80-d incubation. For all but the highest Cu treatments (400 mg kg(-1)) in the coarse-textured Arkport soil, mineralization began without a lag phase and declined over time. No inhibition of mineralization was observed for Zn up to 400 mg kg(-1) in either soil, suggesting differential sensitivity of glyphosate mineralization to the types of metal and soil. Interestingly, Zn appeared to alleviate high-Cu inhibition of mineralization in the Arkport soil. The protective role of Zn against Cu toxicity was also observed in the pure culture study with Pseudomonas aeruginosa, suggesting that increased mineralization rates in high Cu soil with Zn additions might have been due to alleviation of cellular toxicity by Zn rather than a mineralization specific mechanism. Extensive use of glyphosate combined with its reduced degradation in Cu-contaminated, coarse-textured soils may increase glyphosate persistence in soil and consequently facilitate Cu and glyphosate mobilization in the soil environment.
19 CFR 159.38 - Rates for estimated duties.
Code of Federal Regulations, 2011 CFR
2011-04-01
... TREASURY (CONTINUED) LIQUIDATION OF DUTIES Conversion of Foreign Currency § 159.38 Rates for estimated duties. For purposes of calculating estimated duties, the port director shall use the rate or rates... 19 Customs Duties 2 2011-04-01 2011-04-01 false Rates for estimated duties. 159.38 Section...
Estimating induced abortion rates: a review.
Rossier, Clémentine
2003-06-01
Legal abortions are authorized medical procedures, and as such, they are or can be recorded at the health facility where they are performed. The incidence of illegal, often unsafe, induced abortion has to be estimated, however. In the literature, no fewer than eight methods have been used to estimate the frequency of induced abortion: the "illegal abortion provider survey," the "complications statistics" approach, the "mortality statistics" approach, self-reporting techniques, prospective studies, the "residual" method, anonymous third party reports, and experts' estimates. This article describes the methodological requirements of each of these methods and discusses their biases. Empirical records for each method are reviewed, with particular attention paid to the contexts in which the method has been employed successfully. Finally, the choice of an appropriate method of estimation is discussed, depending on the context in which it is to be applied and on the goal of the estimation effort.
Spike sorting of synchronous spikes from local neuron ensembles
Pröpper, Robert; Alle, Henrik; Meier, Philipp; Geiger, Jörg R. P.; Obermayer, Klaus; Munk, Matthias H. J.
2015-01-01
Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons. PMID:26289473
A robust estimator of rainfall rate using differential reflectivity
NASA Technical Reports Server (NTRS)
Gorgucci, Eugenio; Scarchilli, Gianfranco; Chandrasekar, V.
1994-01-01
Conventional estimator of rainfall rate using reflectivity factor and differential reflectivity Z(sub DR) becomes unstable when the measured values of Z(sub DR) are small due to measurement errors. An alternate estimator of rainfall rate using reflectivity factor and Z(sub DR) is derived, so that this estimator is fairly robust over the full dynamic range of reflectivity factor and Z(sub DR). Simulations are used to study the error structure of this robust estimator in comparison with the conventional estimator of rainfall rate. It is shown that the alternate estimator performs better than the conventional estimator of rainfall rate at all rainfall values. In particular the largest improvement of this estimator is proved to be in light rain. The robust estimator is obtained as a direct regression of rainfall rate against reflectivity factor and Z(sub DR) instead of solving for the drop size distribution.
Completely monotone regression estimates of software failure rates
NASA Technical Reports Server (NTRS)
Miller, D. R.; Sofer, A.
1985-01-01
A method for estimating the present failure rate of a program is presented. A crude nonparameter estimate of the failure rate function is obtained from past failure times. This estimate is then smoothed by fitting a completely monotonic function, which is the solution of a quadratic programming problem. The value of the smoothed function at present time is used as the estimate of present failure rate. Results of a Monte Carlo study of performance are given.
Bias in Estimation of Misclassification Rates
ERIC Educational Resources Information Center
Haberman, Shelby J.
2006-01-01
When a simple random sample of size n is employed to establish a classification rule for prediction of a polytomous variable by an independent variable, the best achievable rate of misclassification is higher than the corresponding best achievable rate if the conditional probability distribution is known for the predicted variable given the…
Advances in Glomerular Filtration Rate Estimating Equations
Stevens, Lesley A; Padala, Smita; Levey, Andrew S
2011-01-01
Purpose of review Estimated GFR is now commonly reported by clinical laboratories. Here we review the performance of current creatinine and cystatin C based estimating equations as well as demonstration of their utility in public health and clinical practice. Recent findings Lower levels of GFR are associated with multiple adverse outcomes, including acute kidney injury and medical errors. The new CKD-EPI equation improves performance and risk prediction compared to the MDRD Study equation. Current cystatin C based equations are not accurate in all populations, even in those with reduced muscle mass or chronic illness, where cystatin C would be expected to outperform creatinine. eGFR reporting has led to a greater number of referrals to nephrologists, but the increased numbers do not appear to be excessive or burdensome The MDRD Study equation appears to be able to provide drug dosage adjustments similar to the Cockcroft and Gault. Summary Estimated GFR and their reporting can improve and facilitate clinical practice for chronic kidney disease. Understanding strengths and limitations facilitates their optimal use. Endogenous filtration markers, alone or in combination, that less dependent on non GFR determinants of the filtration markers are necessary to lead to more accurate estimated GFR. PMID:20393287
Estimation of Warfighter Resting Metabolic Rate
2005-04-14
influencing basal metabolic rate in normal adults. Am J Clin Nutr 33:2372-2374, 1980. Daly, J.M.; Heymsfield, S.B.; Head, C.A.; Harvey, L.P.; Nixon, D.W...Reappraisal of the resting metabolic rate of normal young men. Am J Clin Nutr 53(1):21-26, 1991. Cunningham, J.L. A reanalysis of the factors
19 CFR 159.38 - Rates for estimated duties.
Code of Federal Regulations, 2010 CFR
2010-04-01
... duties. For purposes of calculating estimated duties, the port director shall use the rate or rates... 19 Customs Duties 2 2010-04-01 2010-04-01 false Rates for estimated duties. 159.38 Section 159.38 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF...
Estimating mutation rates from paternity casework.
Vicard, P; Dawid, A P; Mortera, J; Lauritzen, S L
2008-01-01
We present a statistical methodology for making inferences about mutation rates from paternity casework. This takes account of a number of sources of potential bias, including hidden mutation, incomplete family triplets, uncertain paternity status and differing maternal and paternal mutation rates, while allowing a wide variety of mutation models. An object-oriented Bayesian network is used to facilitate computation of the likelihood function for the mutation parameters. This can process either full or summary genotypic information, both from complete putative father-mother-child triplets and from defective cases where only the child and one of its parents are observed. We use a dataset from paternity casework to illustrate the effects on inferences about mutation parameters of various types of biases and the mutation model assumed. In particular, we show that there can be relevant information in cases of unconfirmed paternity, and that excluding these, as has generally been done, can lead to biased conclusions.
Simulated data supporting inbreeding rate estimates from incomplete pedigrees
Miller, Mark P.
2017-01-01
This data release includes:(1) The data from simulations used to illustrate the behavior of inbreeding rate estimators. Estimating inbreeding rates is particularly difficult for natural populations because parentage information for many individuals may be incomplete. Our analyses illustrate the behavior of a newly-described inbreeding rate estimator that outperforms previously described approaches in the scientific literature.(2) Python source code ("analytical expressions", "computer simulations", and "empricial data set") that can be used to analyze these data.
Attitude and Trajectory Determination using Magnetometers and Estimated Rates
NASA Technical Reports Server (NTRS)
Schierman, J. D.; Schmidt, D. K.; Deutschmann, J.
1997-01-01
A simultaneous attitude and orbit determination algorithm which uses magnetometer measurements and estimated attitude rates is presented. This is an extension of an algorithm which uses magnetometer and rate gyro measurements. The new algorithm is intended for gyroless spacecraft, or in the case of gyro failures/saturation. Torque control input data is used in forming the rate estimates. Simulation tests of the algorithm are presented. First, tests were performed using the 'true' rate values at each time step. This simulated using accurate gyro measurements. Then, tests were performed estimating the rates. Using estimated rates rather than 'gyro measurements' did not significantly degrade the algorithm's performance if accurate estimates of the initial rates were available. An initial Root-Sum-Square (RSS) position error of 1,400 km was reduced to an average error of approximately 100 km within the first two minutes. The RSS attitude error converged to less than 1.5 degrees within three orbits.
Veit, Julia; Bhattacharyya, Anwesha; Kretz, Robert; Rainer, Gregor
2011-11-01
Entrainment of neural activity to luminance impulses during the refresh of cathode ray tube monitor displays has been observed in the primary visual cortex (V1) of humans and macaque monkeys. This entrainment is of interest because it tends to temporally align and thus synchronize neural responses at the millisecond timescale. Here we show that, in tree shrew V1, both spiking and local field potential activity are also entrained at cathode ray tube refresh rates of 120, 90, and 60 Hz, with weakest but still significant entrainment even at 120 Hz, and strongest entrainment occurring in cortical input layer IV. For both luminance increments ("white" stimuli) and decrements ("black" stimuli), refresh rate had a strong impact on the temporal dynamics of the neural response for subsequent luminance impulses. Whereas there was rapid, strong attenuation of spikes and local field potential to prolonged visual stimuli composed of luminance impulses presented at 120 Hz, attenuation was nearly absent at 60-Hz refresh rate. In addition, neural onset latencies were shortest at 120 Hz and substantially increased, by ∼15 ms, at 60 Hz. In terms of neural response amplitude, black responses dominated white responses at all three refresh rates. However, black/white differences were much larger at 60 Hz than at higher refresh rates, suggesting a mechanism that is sensitive to stimulus timing. Taken together, our findings reveal many similarities between V1 of macaque and tree shrew, while underscoring a greater temporal sensitivity of the tree shrew visual system.
Data-Rate Estimation for Autonomous Receiver Operation
NASA Technical Reports Server (NTRS)
Tkacenko, A.; Simon, M. K.
2005-01-01
In this article, we present a series of algorithms for estimating the data rate of a signal whose admissible data rates are integer base, integer powered multiples of a known basic data rate. These algorithms can be applied to the Electra radio currently used in the Deep Space Network (DSN), which employs data rates having the above relationship. The estimation is carried out in an autonomous setting in which very little a priori information is assumed. It is done by exploiting an elegant property of the split symbol moments estimator (SSME), which is traditionally used to estimate the signal-to-noise ratio (SNR) of the received signal. By quantizing the assumed symbol-timing error or jitter, we present an all-digital implementation of the SSME which can be used to jointly estimate the data rate, SNR, and jitter. Simulation results presented show that these joint estimation algorithms perform well, even in the low SNR regions typically encountered in the DSN.
On the estimation of spread rate for a biological population
Jim Clark; Lajos Horváth; Mark Lewis
2001-01-01
We propose a nonparametric estimator for the rate of spread of an introduced population. We prove that the limit distribution of the estimator is normal or stable, depending on the behavior of the moment generating function. We show that resampling methods can also be used to approximate the distribution of the estimators.
Estimating forest conversion rates with annual forest inventory data
Paul C. Van Deusen; Francis A. Roesch
2009-01-01
The rate of land-use conversion from forest to nonforest or natural forest to forest plantation is of interest for forest certification purposes and also as part of the process of assessing forest sustainability. Conversion rates can be estimated from remeasured inventory plots in general, but the emphasis here is on annual inventory data. A new estimator is proposed...
Predictability of EEG interictal spikes.
Scott, D A; Schiff, S J
1995-01-01
To determine whether EEG spikes are predictable, time series of EEG spike intervals were generated from subdural and depth electrode recordings from four patients. The intervals between EEG spikes were hand edited to ensure high accuracy and eliminate false positive and negative spikes. Spike rates (per minute) were generated from longer time series, but for these data hand editing was usually not feasible. Linear and nonlinear models were fit to both types of data. One patient had no linear or nonlinear predictability, two had predictability that could be well accounted for with a linear stochastic model, and one had a degree of nonlinear predictability for both interval and rate data that no linear model could adequately account for. PMID:8580318
Guo, Zifeng; Slafer, Gustavo A; Schnurbusch, Thorsten
2016-01-01
Spike fertility traits are critical attributes for grain yield in wheat (Triticum aestivum L.). Here, we examine the genotypic variation in three important traits: maximum number of floret primordia, number of fertile florets, and number of grains. We determine their relationship in determining spike fertility in 30 genotypes grown under two contrasting conditions: field and greenhouse. The maximum number of floret primordia per spikelet (MFS), fertile florets per spikelet (FFS), and number of grains per spikelet (GS) not only exhibited large genotypic variation in both growth conditions and across all spikelet positions studied, but also displayed moderate levels of heritability. FFS was closely associated with floret survival and only weakly related to MFS. We also found that the post-anthesis process of grain set/abortion was important in determining genotypic variation in GS; an increase in GS was mainly associated with improved grain survival. Ovary size at anthesis was associated with both floret survival (pre-anthesis) and grain survival (post-anthesis), and was thus believed to ‘connect’ the two traits. In this work, proximal florets (i.e. the first three florets from the base of a spikelet: F1, F2, and F3) produced fertile florets and set grains in most cases. The ovary size of more distal florets (F4 and beyond) seemed to act as a decisive factor for grain setting and effectively reflected pre-anthesis floret development. In both growth conditions, GS positively correlated with ovary size of florets in the distal position (F4), suggesting that assimilates allocated to distal florets may play a critical role in regulating grain set. PMID:27279276
Adaptive Estimation of Intravascular Shear Rate Based on Parameter Optimization
NASA Astrophysics Data System (ADS)
Nitta, Naotaka; Takeda, Naoto
2008-05-01
The relationships between the intravascular wall shear stress, controlled by flow dynamics, and the progress of arteriosclerosis plaque have been clarified by various studies. Since the shear stress is determined by the viscosity coefficient and shear rate, both factors must be estimated accurately. In this paper, an adaptive method for improving the accuracy of quantitative shear rate estimation was investigated. First, the parameter dependence of the estimated shear rate was investigated in terms of the differential window width and the number of averaged velocity profiles based on simulation and experimental data, and then the shear rate calculation was optimized. The optimized result revealed that the proposed adaptive method of shear rate estimation was effective for improving the accuracy of shear rate calculation.
NASA Astrophysics Data System (ADS)
Olsson, Per-Ivar; Fiandaca, Gianluca; Larsen, Jakob Juul; Dahlin, Torleif; Auken, Esben
2016-11-01
potential readings are previously used for current injection, also for simple contact resistance measurements. We developed a drift-removal scheme that models the polarization effect and efficiently allows for preserving the shape of the IP responses at late times. Uncertainty estimates are essential in the inversion of IP data. Therefore, in the final step of the data processing, we estimate the data standard deviation based on the data variability within the IP gates and the misfit of the background drift removal Overall, the removal of harmonic noise, spikes, self-potential drift, tapered windowing and the uncertainty estimation allows for doubling the usable range of TDIP data to almost four decades in time (corresponding to four decades in frequency), which will significantly advance the applicability of the IP method.
Three-Axis Attitude Estimation Using Rate-Integrating Gyroscopes
NASA Technical Reports Server (NTRS)
Crassidis, John L.; Markley, F. Landis
2016-01-01
Traditionally, attitude estimation has been performed using a combination of external attitude sensors and internal three-axis gyroscopes. There are many studies of three-axis attitude estimation using gyros that read angular rates. Rate-integrating gyros measure integrated rates or angular displacements, but three-axis attitude estimation using these types of gyros has not been as fully investigated. This paper derives a Kalman filtering framework for attitude estimation using attitude sensors coupled with rate- integrating gyroscopes. In order to account for correlations introduced by using these gyros, the state vector must be augmented, compared with filters using traditional gyros that read angular rates. Two filters are derived in this paper. The first uses an augmented state-vector form that estimates attitude, gyro biases, and gyro angular displacements. The second ignores correlations, leading to a filter that estimates attitude and gyro biases only. Simulation comparisons are shown for both filters. The work presented in this paper focuses only on attitude estimation using rate-integrating gyros, but it can easily be extended to other applications such as inertial navigation, which estimates attitude and position.
NASA Astrophysics Data System (ADS)
Xiao, Mengchu
The Penobscot study area is located offshore Nova Scotia, Canada. There are two wells, which penetrate the highest potentially commercial bodies in the Abenaki Formation. In order to investigate the potential for locating additional hydrocarbon reservoirs, well log data was used and the Penobscot 3D seismic dataset was analyzed using Constrained Sparse Spike Inversion. From the well log data, low GR and SP values are an indication of a permeable sand layer, which provides the target zone in this study. Impedance - porosity crossplots gave the relationship between impedance and porosity, where a low impedance sand layer is correlated with high porosity. It was found that the target sand layer has low impedance, a feature recognizable from the inversion results. The porosity of the whole sand layer calculated by the linear function from the relationship between impedance and porosity. The calculation of thickness of this sand layer from maps representing different impedance intervals provided numeric evidence to show there is a low impedance sand layer in the well L-30. The pore thickness map results indicate there is greater pore thickness in well L-30 than B-41. It appears that the company drilled at the optimal location for the initial (L-30) well, and tested the extent of potential reservoir rock with the second (B-41) well. The potential reservoir is apparently fairly small, and restricted to the area around L-30. There may or may not be value in testing another location across a fault, but the rock behind the fault is likely not as high quality as at L-30 and the high-quality regions are small in size and not connected.
Estimation of kinetic rates in batch Thiobacillus ferrooxidans cultures.
Biagiola, S; Solsona, J; Milocco, R
2001-11-17
In this work, the key problem of estimation in bioprocesses when no structural model is available is dealt with. A nonlinear observer-based algorithm is developed in order to estimate kinetic rates in batch bioreactors. The algorithm uses the measurements of biomass concentration and either substrate concentration or redox potential to perform the estimation of the respective specific kinetic rates. For this purpose, a general mathematical model description of the process is provided. The estimation algorithm design is based on a nonlinear reduced-order observer. The observer performance is validated with experimental results on a Thiobacillus ferrooxidans batch culture.
Estimating monotonic rates from biological data using local linear regression.
Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R
2017-03-01
Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.
Time-resolved and time-scale adaptive measures of spike train synchrony.
Kreuz, Thomas; Chicharro, Daniel; Greschner, Martin; Andrzejak, Ralph G
2011-01-30
A wide variety of approaches to estimate the degree of synchrony between two or more spike trains have been proposed. One of the most recent methods is the ISI-distance which extracts information from the interspike intervals (ISIs) by evaluating the ratio of the instantaneous firing rates. In contrast to most previously proposed measures it is parameter free and time-scale independent. However, it is not well suited to track changes in synchrony that are based on spike coincidences. Here we propose the SPIKE-distance, a complementary measure which is sensitive to spike coincidences but still shares the fundamental advantages of the ISI-distance. In particular, it is easy to visualize in a time-resolved manner and can be extended to a method that is also applicable to larger sets of spike trains. We show the merit of the SPIKE-distance using both simulated and real data. Copyright © 2010 Elsevier B.V. All rights reserved.
Detecting joint pausiness in parallel spike trains.
Gärtner, Matthias; Duvarci, Sevil; Roeper, Jochen; Schneider, Gaby
2017-06-15
Transient periods with reduced neuronal discharge - called 'pauses' - have recently gained increasing attention. In dopamine neurons, pauses are considered important teaching signals, encoding negative reward prediction errors. Particularly simultaneous pauses are likely to have increased impact on information processing. Available methods for detecting joint pausing analyze temporal overlap of pauses across spike trains. Such techniques are threshold dependent and can fail to identify joint pauses that are easily detectable by eye, particularly in spike trains with different firing rates. We introduce a new statistic called pausiness that measures the degree of synchronous pausing in spike train pairs and avoids threshold-dependent identification of specific pauses. A new graphic termed the cross-pauseogram compares the joint pausiness of two spike trains with its time shifted analogue, such that a (pausiness) peak indicates joint pausing. When assessing significance of pausiness peaks, we use a stochastic model with synchronous spikes to disentangle joint pausiness arising from synchronous spikes from additional 'joint excess pausiness' (JEP). Parameter estimates are obtained from auto- and cross-correlograms, and statistical significance is assessed by comparison to simulated cross-pauseograms. Our new method was applied to dopamine neuron pairs recorded in the ventral tegmental area of awake behaving mice. Significant JEP was detected in about 20% of the pairs. Given the neurophysiological importance of pauses and the fact that neurons integrate multiple inputs, our findings suggest that the analysis of JEP can reveal interesting aspects in the activity of simultaneously recorded neurons. Copyright © 2017 Elsevier B.V. All rights reserved.
Günay, Cengiz; Sieling, Fred H.; Dharmar, Logesh; Lin, Wei-Hsiang; Wolfram, Verena; Marley, Richard
2015-01-01
Studying ion channel currents generated distally from the recording site is difficult because of artifacts caused by poor space clamp and membrane filtering. A computational model can quantify artifact parameters for correction by simulating the currents only if their exact anatomical location is known. We propose that the same artifacts that confound current recordings can help pinpoint the source of those currents by providing a signature of the neuron’s morphology. This method can improve the recording quality of currents initiated at the spike initiation zone (SIZ) that are often distal to the soma in invertebrate neurons. Drosophila being a valuable tool for characterizing ion currents, we estimated the SIZ location and quantified artifacts in an identified motoneuron, aCC/MN1-Ib, by constructing a novel multicompartmental model. Initial simulation of the measured biophysical channel properties in an isopotential Hodgkin-Huxley type neuron model partially replicated firing characteristics. Adding a second distal compartment, which contained spike-generating Na+ and K+ currents, was sufficient to simulate aCC’s in vivo activity signature. Matching this signature using a reconstructed morphology predicted that the SIZ is on aCC’s primary axon, 70 μm after the most distal dendritic branching point. From SIZ to soma, we observed and quantified selective morphological filtering of fast activating currents. Non-inactivating K+ currents are filtered ∼3 times less and despite their large magnitude at the soma they could be as distal as Na+ currents. The peak of transient component (NaT) of the voltage-activated Na+ current is also filtered more than the magnitude of slower persistent component (NaP), which can contribute to seizures. The corrected NaP/NaT ratio explains the previously observed discrepancy when the same channel is expressed in different cells. In summary, we used an in vivo signature to estimate ion channel location and recording artifacts
Angular Rate Estimation Using a Distributed Set of Accelerometers
Park, Sungsu; Hong, Sung Kyung
2011-01-01
A distributed set of accelerometers based on the minimum number of 12 accelerometers allows for computation of the magnitude of angular rate without using the integration operation. However, it is not easy to extract the magnitude of angular rate in the presence of the accelerometer noises, and even worse, it is difficult to determine the direction of a rotation because the angular rate is present in its quadratic form within the inertial measurement system equations. In this paper, an extended Kalman filter scheme to correctly estimate both the direction and magnitude of the angular rate through fusion of the angular acceleration and quadratic form of the angular rate is proposed. We also provide observability analysis for the general distributed accelerometers-based inertial measurement unit, and show that the angular rate can be correctly estimated by general nonlinear state estimators such as an extended Kalman filter, except under certain extreme conditions. PMID:22346651
Estimating survival rates with age-structure data
Udevitz, M.S.; Ballachey, B.E.
1998-01-01
We developed a general statistical model that provides a comprehensive framework for inference about survival rates based on standing age-structure and ages-at-death data. Previously available estimators are maximum likelihood under the general model, but they use only 1 type of data and require the assumption of a stable age structure and a known population growth rate. We used the general model to derive new survival rate estimators that use both types of data and require only the assumption of a stable age structure or a known population growth rate. Our likelihood-based approach allows use of standard model-selection procedures to test hypotheses about age-structure stability, population growth rates, and age-related patterns in survival. We used this approach to estimate survival rates for female sea otters (Enhydra lutris) in Prince William Sound, Alaska.
Geodetic slip rate estimates in California, and their uncertainties
NASA Astrophysics Data System (ADS)
Evans, E. L.
2016-12-01
Current understanding of the seismic potential of faults in California is limited in part by our ability to resolve spatial and temporal changes in fault slip rates across the Pacific-North American plate boundary, and quantify their uncertainties. Fault slip rate can be estimated by modeling fault systems, based on space geodetic measurements of surface ground displacement (GPS and InSAR). However, models that include elastic deformation due to locked faults require fault geometries to be prescribed, and geodetic slip rate estimates may vary widely due to measurement and epistemic (model) uncertainties. To examine published geodetic slip rate estimates in California and quantify variability among models, we compile 31 published geodetic slip rate studies in California and Nevada. Because deformation models may vary in the number of faults represented and the precise location of faults, we combine published geodetic slip rate estimates on a georeferenced grid and compare models spatially. Within each grid cell, a number of metrics are considered based on the suite of fault slip rates in the cell. These metrics include geometric moment (potency), strain and rotation, and variation among models. This approach assumes that all published geodetic slip rate estimates are equally valid, and therefore this variability among models serves as a proxy for epistemic uncertainties in geodetic slip rates: we find an average standard deviation in potency rate of 1.5×106 m3/yr for cells of 725 km2 (1,365 cells), which corresponds to 2 mm/yr of model uncertainty on a given slip rate. These uncertainties may be incorporated into hazard estimates, enable rigorous comparison with geologic slip rates, and used to systematically identify regions that may require more careful consideration in terms of modeling available geologic and geodetic data.
Estimating the encounter rate variance in distance sampling
Fewster, R.M.; Buckland, S.T.; Burnham, K.P.; Borchers, D.L.; Jupp, P.E.; Laake, J.L.; Thomas, L.
2009-01-01
The dominant source of variance in line transect sampling is usually the encounter rate variance. Systematic survey designs are often used to reduce the true variability among different realizations of the design, but estimating the variance is difficult and estimators typically approximate the variance by treating the design as a simple random sample of lines. We explore the properties of different encounter rate variance estimators under random and systematic designs. We show that a design-based variance estimator improves upon the model-based estimator of Buckland et al. (2001, Introduction to Distance Sampling. Oxford: Oxford University Press, p. 79) when transects are positioned at random. However, if populations exhibit strong spatial trends, both estimators can have substantial positive bias under systematic designs. We show that poststratification is effective in reducing this bias. ?? 2008, The International Biometric Society.
Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements
NASA Technical Reports Server (NTRS)
Wang, Jianxin; Fisher, Brad L.; Wolff, David B.
2007-01-01
This paper describes the cubic spline based operational system for the generation of the TRMM one-minute rain rate product 2A-56 from Tipping Bucket (TB) gauge measurements. Methodological issues associated with applying the cubic spline to the TB gauge rain rate estimation are closely examined. A simulated TB gauge from a Joss-Waldvogel (JW) disdrometer is employed to evaluate effects of time scales and rain event definitions on errors of the rain rate estimation. The comparison between rain rates measured from the JW disdrometer and those estimated from the simulated TB gauge shows good overall agreement; however, the TB gauge suffers sampling problems, resulting in errors in the rain rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When one minute rain rates are averaged to 4-7 minute or longer time scales, the errors dramatically reduce. The rain event duration is very sensitive to the event definition but the event rain total is rather insensitive, provided that the events with less than 1 millimeter rain totals are excluded. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-minute TB rain rates higher and lower than 3 mm per hour, respectively. These errors decrease to 5% and 14% when TB rain rates are used at 7-minute scale. The radar reflectivity-rainrate (Ze-R) distributions drawn from large amount of 7-minute TB rain rates and radar reflectivity data are mostly insensitive to the event definition.
Inclination Dependence of Estimated Galaxy Masses and Star Formation Rates
NASA Astrophysics Data System (ADS)
Hernandez, Betsy; Maller, Ariyeh; McKernan, Barry; Ford, Saavik
2016-01-01
We examine the inclination dependence of inferred star formation rates and galaxy mass estimates in the Sloan Digital Sky Survey by combining the disk/bulge de-convolved catalog of Simard et al 2011 with stellar mass estimates catalog of Mendel et al 2014 and star formation rates measured from spectra by Brinchmann et al 2004. We know that optical star formation indicators are reddened by dust, but calculated star formation rates and stellar mass estimates should account for this. However, we find that face-on galaxies have a higher calculated average star formation rates than edge-on galaxies. We also find edge-on galaxies have ,on average, slightly smaller but similar estimated masses to face-on galaxies, suggesting that there are issues with the applied dust corrections for both models.
Estimating the exceedance probability of rain rate by logistic regression
NASA Technical Reports Server (NTRS)
Chiu, Long S.; Kedem, Benjamin
1990-01-01
Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.
Estimating the exceedance probability of rain rate by logistic regression
NASA Technical Reports Server (NTRS)
Chiu, Long S.; Kedem, Benjamin
1990-01-01
Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.
Effect of impactor area on collision rate estimates
Canavan, G.H.
1996-08-01
Analytic and numercial estimates provide an assessment of the effect of impactor area on space debris collision rates, which is sufficiently small and insensitive to parameters of inerest that it could be neglected or corrected.
Estimating meiotic gene conversion rates from population genetic data.
Gay, J; Myers, S; McVean, G
2007-10-01
Gene conversion plays an important part in shaping genetic diversity in populations, yet estimating the rate at which it occurs is difficult because of the short lengths of DNA involved. We have developed a new statistical approach to estimating gene conversion rates from genetic variation, by extending an existing model for haplotype data in the presence of crossover events. We show, by simulation, that when the rate of gene conversion events is at least comparable to the rate of crossover events, the method provides a powerful approach to the detection of gene conversion and estimation of its rate. Application of the method to data from the telomeric X chromosome of Drosophila melanogaster, in which crossover activity is suppressed, indicates that gene conversion occurs approximately 400 times more often than crossover events. We also extend the method to estimating variable crossover and gene conversion rates and estimate the rate of gene conversion to be approximately 1.5 times higher than the crossover rate in a region of human chromosome 1 with known recombination hotspots.
Estimates of loss rates of jaw tags on walleyes
Newman, Steven P.; Hoff, Michael H.
1998-01-01
The rate of jaw tag loss was evaluated for walleye Stizostedion vitreum in Escanaba Lake, Wisconsin. We estimated tag loss using two recapture methods, a creel census and fykenetting. Average annual tag loss estimates were 17.5% for fish recaptured by anglers and 27.8% for fish recaptured in fyke nets. However, fyke-net data were biased by tag loss during netting. The loss rate of jaw tags increased with time and walleye length.
Ryskamp, Daniel A.; Witkovsky, Paul; Barabas, Peter; Huang, Wei; Koehler, Christopher; Akimov, Nikolay P.; Lee, Suk Hee; Chauhan, Shiwani; Xing, Wei; Rentería, René C.; Liedtke, Wolfgang; Krizaj, David
2011-01-01
Sustained increase in intraocular pressure represents a major risk factor for eye disease yet the cellular mechanisms of pressure transduction in the posterior eye are essentially unknown. Here we show that the mouse retina expresses mRNA and protein for the polymodal TRPV4 cation channel known to mediate osmo- and mechanotransduction. TRPV4 antibodies labeled perikarya, axons and dendrites of retinal ganglion cells (RGCs) and intensely immunostained the optic nerve head. Müller glial cells, but not retinal astrocytes or microglia, also expressed TRPV4 immunoreactivity. The selective TRPV4 agonists 4α-PDD and GSK1016790A elevated [Ca2+]i in dissociated RGCs in a dose-dependent manner whereas the TRPV1 agonist capsaicin had no effect on [Ca2+]RGC. Exposure to hypotonic stimulation evoked robust increases in [Ca2+]RGC. RGC responses to TRPV4-selective agonists and hypotonic stimulation were absent in Ca2+-free saline and were antagonized by the nonselective TRP channel antagonists Ruthenium Red and gadolinium, but were unaffected by the TRPV1 antagonist capsazepine. TRPV4-selective agonists increased the spiking frequency recorded from intact retinas recorded with multielectrode arrays. Sustained exposure to TRPV4 agonists evoked dose-dependent apoptosis of RGCs. Our results demonstrate functional TRPV4 expression in RGCs and suggest that its activation mediates response to membrane stretch leading to elevated [Ca2+]i and augmented excitability. Excessive Ca2+ influx through TRPV4 predisposes RGCs to activation of Ca2+-dependent pro-apoptotic signaling pathways, indicating that TRPV4 is a component of the response mechanism to pathological elevations of intraocular pressure. PMID:21562271
McCormick, Joshua L.; Quist, Michael C.; Schill, Daniel J.
2012-01-01
Roving–roving and roving–access creel surveys are the primary techniques used to obtain information on harvest of Chinook salmon Oncorhynchus tshawytscha in Idaho sport fisheries. Once interviews are conducted using roving–roving or roving–access survey designs, mean catch rate can be estimated with the ratio-of-means (ROM) estimator, the mean-of-ratios (MOR) estimator, or the MOR estimator with exclusion of short-duration (≤0.5 h) trips. Our objective was to examine the relative bias and precision of total catch estimates obtained from use of the two survey designs and three catch rate estimators for Idaho Chinook salmon fisheries. Information on angling populations was obtained by direct visual observation of portions of Chinook salmon fisheries in three Idaho river systems over an 18-d period. Based on data from the angling populations, Monte Carlo simulations were performed to evaluate the properties of the catch rate estimators and survey designs. Among the three estimators, the ROM estimator provided the most accurate and precise estimates of mean catch rate and total catch for both roving–roving and roving–access surveys. On average, the root mean square error of simulated total catch estimates was 1.42 times greater and relative bias was 160.13 times greater for roving–roving surveys than for roving–access surveys. Length-of-stay bias and nonstationary catch rates in roving–roving surveys both appeared to affect catch rate and total catch estimates. Our results suggest that use of the ROM estimator in combination with an estimate of angler effort provided the least biased and most precise estimates of total catch for both survey designs. However, roving–access surveys were more accurate than roving–roving surveys for Chinook salmon fisheries in Idaho.
Estimating survival rates from banding of adult and juvenile birds
Johnson, D.H.
1974-01-01
The restrictive assumptions required by most available methods for estimating survival probabilities render them unsuitable for analyzing real banding data. A model is proposed which allows survival rates and recovery rates to vary with the calendar year, and also allows juveniles to have rates different from adults. In addition to survival rates and recovery rates, the differential vulnerability factors of juveniles relative to adults are estimated. Minimum values of the variances of the estimators are also given. The new procedure is applied to sets of duck and goose data in which reasonably large numbers of adult and juvenile birds were banded. The results are shown to be generally comparable to those procured by other methods, but, in addition, insight into the extent of annual variation is gained. Combining data from adults and juveniles also increases the effective sample size, since the juveniles are assumed to enter the adult age class after surviving their initial year.
Propagation of rating curve uncertainty in design flood estimation
NASA Astrophysics Data System (ADS)
Steinbakk, G. H.; Thorarinsdottir, T. L.; Reitan, T.; Schlichting, L.; Hølleland, S.; Engeland, K.
2016-09-01
Statistical flood frequency analysis is commonly performed based on a set of annual maximum discharge values which are derived from stage measurements via a stage-discharge rating curve model. Such design flood estimation techniques often ignore the uncertainty in the underlying rating curve model. Using data from eight gauging stations in Norway, we investigate the effect of curve and sample uncertainty on design flood estimation by combining results from a Bayesian multisegment rating curve model and a Bayesian flood frequency analysis. We find that sample uncertainty is the main contributor to the design flood estimation uncertainty. However, under extrapolation of the rating curve, the uncertainty bounds for both the rating curve model and the flood frequency analysis are highly skewed and ignoring these features may underestimate the potential risk of flooding. We expect this effect to be even more pronounced in arid and semiarid climates with a higher variability in floods.
Estimating the normal background rate of species extinction.
De Vos, Jurriaan M; Joppa, Lucas N; Gittleman, John L; Stephens, Patrick R; Pimm, Stuart L
2015-04-01
A key measure of humanity's global impact is by how much it has increased species extinction rates. Familiar statements are that these are 100-1000 times pre-human or background extinction levels. Estimating recent rates is straightforward, but establishing a background rate for comparison is not. Previous researchers chose an approximate benchmark of 1 extinction per million species per year (E/MSY). We explored disparate lines of evidence that suggest a substantially lower estimate. Fossil data yield direct estimates of extinction rates, but they are temporally coarse, mostly limited to marine hard-bodied taxa, and generally involve genera not species. Based on these data, typical background loss is 0.01 genera per million genera per year. Molecular phylogenies are available for more taxa and ecosystems, but it is debated whether they can be used to estimate separately speciation and extinction rates. We selected data to address known concerns and used them to determine median extinction estimates from statistical distributions of probable values for terrestrial plants and animals. We then created simulations to explore effects of violating model assumptions. Finally, we compiled estimates of diversification-the difference between speciation and extinction rates for different taxa. Median estimates of extinction rates ranged from 0.023 to 0.135 E/MSY. Simulation results suggested over- and under-estimation of extinction from individual phylogenies partially canceled each other out when large sets of phylogenies were analyzed. There was no evidence for recent and widespread pre-human overall declines in diversity. This implies that average extinction rates are less than average diversification rates. Median diversification rates were 0.05-0.2 new species per million species per year. On the basis of these results, we concluded that typical rates of background extinction may be closer to 0.1 E/MSY. Thus, current extinction rates are 1,000 times higher than natural
Estimating Divergence Dates and Substitution Rates in the Drosophila Phylogeny
Obbard, Darren J.; Maclennan, John; Kim, Kang-Wook; Rambaut, Andrew; O’Grady, Patrick M.; Jiggins, Francis M.
2012-01-01
An absolute timescale for evolution is essential if we are to associate evolutionary phenomena, such as adaptation or speciation, with potential causes, such as geological activity or climatic change. Timescales in most phylogenetic studies use geologically dated fossils or phylogeographic events as calibration points, but more recently, it has also become possible to use experimentally derived estimates of the mutation rate as a proxy for substitution rates. The large radiation of drosophilid taxa endemic to the Hawaiian islands has provided multiple calibration points for the Drosophila phylogeny, thanks to the "conveyor belt" process by which this archipelago forms and is colonized by species. However, published date estimates for key nodes in the Drosophila phylogeny vary widely, and many are based on simplistic models of colonization and coalescence or on estimates of island age that are not current. In this study, we use new sequence data from seven species of Hawaiian Drosophila to examine a range of explicit coalescent models and estimate substitution rates. We use these rates, along with a published experimentally determined mutation rate, to date key events in drosophilid evolution. Surprisingly, our estimate for the date for the most recent common ancestor of the genus Drosophila based on mutation rate (25–40 Ma) is closer to being compatible with independent fossil-derived dates (20–50 Ma) than are most of the Hawaiian-calibration models and also has smaller uncertainty. We find that Hawaiian-calibrated dates are extremely sensitive to model choice and give rise to point estimates that range between 26 and 192 Ma, depending on the details of the model. Potential problems with the Hawaiian calibration may arise from systematic variation in the molecular clock due to the long generation time of Hawaiian Drosophila compared with other Drosophila and/or uncertainty in linking island formation dates with colonization dates. As either source of error will
Estimating divergence dates and substitution rates in the Drosophila phylogeny.
Obbard, Darren J; Maclennan, John; Kim, Kang-Wook; Rambaut, Andrew; O'Grady, Patrick M; Jiggins, Francis M
2012-11-01
An absolute timescale for evolution is essential if we are to associate evolutionary phenomena, such as adaptation or speciation, with potential causes, such as geological activity or climatic change. Timescales in most phylogenetic studies use geologically dated fossils or phylogeographic events as calibration points, but more recently, it has also become possible to use experimentally derived estimates of the mutation rate as a proxy for substitution rates. The large radiation of drosophilid taxa endemic to the Hawaiian islands has provided multiple calibration points for the Drosophila phylogeny, thanks to the "conveyor belt" process by which this archipelago forms and is colonized by species. However, published date estimates for key nodes in the Drosophila phylogeny vary widely, and many are based on simplistic models of colonization and coalescence or on estimates of island age that are not current. In this study, we use new sequence data from seven species of Hawaiian Drosophila to examine a range of explicit coalescent models and estimate substitution rates. We use these rates, along with a published experimentally determined mutation rate, to date key events in drosophilid evolution. Surprisingly, our estimate for the date for the most recent common ancestor of the genus Drosophila based on mutation rate (25-40 Ma) is closer to being compatible with independent fossil-derived dates (20-50 Ma) than are most of the Hawaiian-calibration models and also has smaller uncertainty. We find that Hawaiian-calibrated dates are extremely sensitive to model choice and give rise to point estimates that range between 26 and 192 Ma, depending on the details of the model. Potential problems with the Hawaiian calibration may arise from systematic variation in the molecular clock due to the long generation time of Hawaiian Drosophila compared with other Drosophila and/or uncertainty in linking island formation dates with colonization dates. As either source of error will
The Computational Structure of Spike Trains
Haslinger, Robert; Klinkner, Kristina Lisa; Shalizi, Cosma Rohilla
2010-01-01
Neurons perform computations, and convey the results of those computations through the statistical structure of their output spike trains. Here we present a practical method, grounded in the information-theoretic analysis of prediction, for inferring a minimal representation of that structure and for characterizing its complexity. Starting from spike trains, our approach finds their causal state models (CSMs), the minimal hidden Markov models or stochastic automata capable of generating statistically-identical time series. We then use these CSMs to objectively quantify both the generalizable structure and the idiosyncratic randomness of the spike train. Specifically, we show that the expected algorithmic information content (the information needed to describe the spike train exactly) can be split into three parts describing (1) the time-invariant structure (complexity) of the minimal spike-generating process, which describes the spike train statistically, (2) the randomness (internal entropy rate) of the minimal spike-generating process, and (3) a residual pure noise term not described by the minimal spike generating process. We use CSMs to approximate each of these quantities. The CSMs are inferred non-parametrically from the data, making only mild regularity assumptions, via the Causal State Splitting Reconstruction (CSSR) algorithm. The methods presented here complement more traditional spike train analyses by describing not only spiking probability, and spike train entropy, but also the complexity of a spike train’s structure. We demonstrate our approach using both simulated spike trains and experimental data recorded in rat barrel cortex during vibrissa stimulation. PMID:19764880
Estimating infectivity rates and attack windows for two viruses.
Zhang, J; Noe, D A; Wu, J; Bailer, A J; Wright, S E
2012-12-01
Cells exist in an environment in which they are simultaneously exposed to a number of viral challenges. In some cases, infection by one virus may preclude infection by other viruses. Under the assumption of independent times until infection by two viruses, a procedure is presented to estimate the infectivity rates along with the time window during which a cell might be susceptible to infection by multiple viruses. A test for equal infectivity rates is proposed and interval estimates of parameters are derived. Additional hypothesis tests of potential interest are also presented. The operating characteristics of these tests and the estimation procedure are explored in simulation studies.
Estimation of the Dose and Dose Rate Effectiveness Factor
NASA Technical Reports Server (NTRS)
Chappell, L.; Cucinotta, F. A.
2013-01-01
Current models to estimate radiation risk use the Life Span Study (LSS) cohort that received high doses and high dose rates of radiation. Transferring risks from these high dose rates to the low doses and dose rates received by astronauts in space is a source of uncertainty in our risk calculations. The solid cancer models recommended by BEIR VII [1], UNSCEAR [2], and Preston et al [3] is fitted adequately by a linear dose response model, which implies that low doses and dose rates would be estimated the same as high doses and dose rates. However animal and cell experiments imply there should be curvature in the dose response curve for tumor induction. Furthermore animal experiments that directly compare acute to chronic exposures show lower increases in tumor induction than acute exposures. A dose and dose rate effectiveness factor (DDREF) has been estimated and applied to transfer risks from the high doses and dose rates of the LSS cohort to low doses and dose rates such as from missions in space. The BEIR VII committee [1] combined DDREF estimates using the LSS cohort and animal experiments using Bayesian methods for their recommendation for a DDREF value of 1.5 with uncertainty. We reexamined the animal data considered by BEIR VII and included more animal data and human chromosome aberration data to improve the estimate for DDREF. Several experiments chosen by BEIR VII were deemed inappropriate for application to human risk models of solid cancer risk. Animal tumor experiments performed by Ullrich et al [4], Alpen et al [5], and Grahn et al [6] were analyzed to estimate the DDREF. Human chromosome aberration experiments performed on a sample of astronauts within NASA were also available to estimate the DDREF. The LSS cohort results reported by BEIR VII were combined with the new radiobiology results using Bayesian methods.
Estimating residual fault hitting rates by recapture sampling
NASA Technical Reports Server (NTRS)
Lee, Larry; Gupta, Rajan
1988-01-01
For the recapture debugging design introduced by Nayak (1988) the problem of estimating the hitting rates of the faults remaining in the system is considered. In the context of a conditional likelihood, moment estimators are derived and are shown to be asymptotically normal and fully efficient. Fixed sample properties of the moment estimators are compared, through simulation, with those of the conditional maximum likelihood estimators. Properties of the conditional model are investigated such as the asymptotic distribution of linear functions of the fault hitting frequencies and a representation of the full data vector in terms of a sequence of independent random vectors. It is assumed that the residual hitting rates follow a log linear rate model and that the testing process is truncated when the gaps between the detection of new errors exceed a fixed amount of time.
Mortality rate and confidence interval estimation in humanitarian emergencies.
Sullivan, Kevin; Hossain, S M Moazzem; Woodruff, Bradley A
2010-01-01
Surveys are conducted frequently in humanitarian emergencies to assess the health status of the population. Most often, they employ complex sample designs, such as cluster sampling. Mortality is an indicator commonly estimated in such surveys. Confidence limits provide information on the precision of the estimate and it is important to ensure that confidence limits for a mortality rate account for the survey design and utilise an acceptable methodology. This paper describes the calculation of confidence limits for mortality rates from surveys using complex sampling designs and a variety of software programmes and methods. It contains an example that makes use of the SAS, SPSS, and Epi Info software programmes. Of the three confidence interval methods examined--the ratio command approach, the modified rate approach, and the modified proportion approach--the paper recommends the ratio command approach to estimate mortality rates with confidence limits.
Estimation of death rates in US states with small subpopulations.
Voulgaraki, Anastasia; Wei, Rong; Kedem, Benjamin
2015-05-20
In US states with small subpopulations, the observed mortality rates are often zero, particularly among young ages. Because in life tables, death rates are reported mostly on a log scale, zero mortality rates are problematic. To overcome the observed zero death rates problem, appropriate probability models are used. Using these models, observed zero mortality rates are replaced by the corresponding expected values. This enables logarithmic transformations and, in some cases, the fitting of the eight-parameter Heligman-Pollard model to produce mortality estimates for ages 0-130 years, a procedure illustrated in terms of mortality data from several states.
Error Rate Estimation in Quantum Key Distribution with Finite Resources
NASA Astrophysics Data System (ADS)
Lu, Zhao; Shi, Jian-Hong; Li, Feng-Guang
2017-04-01
The goal of quantum key distribution (QKD) is to generate secret key shared between two distant players, Alice and Bob. We present the connection between sampling rate and erroneous judgment probability when estimating error rate with random sampling method, and propose a method to compute optimal sampling rate, which can maximize final secure key generation rate. These results can be applied to choose the optimal sampling rate and improve the performance of QKD system with finite resources. Supported by the National Natural Science Foundation of China under Grant Nos. U1304613 and 11204379
Estimating Children's Soil/Dust Ingestion Rates through ...
Background: Soil/dust ingestion rates are important variables in assessing children’s health risks in contaminated environments. Current estimates are based largely on soil tracer methodology, which is limited by analytical uncertainty, small sample size, and short study duration. Objectives: The objective was to estimate site-specific soil/dust ingestion rates through reevaluation of the lead absorption dose–response relationship using new bioavailability data from the Bunker Hill Mining and Metallurgical Complex Superfund Site (BHSS) in Idaho, USA. Methods: The U.S. Environmental Protection Agency (EPA) in vitro bioavailability methodology was applied to archived BHSS soil and dust samples. Using age-specific biokinetic slope factors, we related bioavailable lead from these sources to children’s blood lead levels (BLLs) monitored during cleanup from 1988 through 2002. Quantitative regression analyses and exposure assessment guidance were used to develop candidate soil/dust source partition scenarios estimating lead intake, allowing estimation of age-specific soil/dust ingestion rates. These ingestion rate and bioavailability estimates were simultaneously applied to the U.S. EPA Integrated Exposure Uptake Biokinetic Model for Lead in Children to determine those combinations best approximating observed BLLs. Results: Absolute soil and house dust bioavailability averaged 33% (SD ± 4%) and 28% (SD ± 6%), respectively. Estimated BHSS age-specific soil/du
Estimating Children's Soil/Dust Ingestion Rates through ...
Background: Soil/dust ingestion rates are important variables in assessing children’s health risks in contaminated environments. Current estimates are based largely on soil tracer methodology, which is limited by analytical uncertainty, small sample size, and short study duration. Objectives: The objective was to estimate site-specific soil/dust ingestion rates through reevaluation of the lead absorption dose–response relationship using new bioavailability data from the Bunker Hill Mining and Metallurgical Complex Superfund Site (BHSS) in Idaho, USA. Methods: The U.S. Environmental Protection Agency (EPA) in vitro bioavailability methodology was applied to archived BHSS soil and dust samples. Using age-specific biokinetic slope factors, we related bioavailable lead from these sources to children’s blood lead levels (BLLs) monitored during cleanup from 1988 through 2002. Quantitative regression analyses and exposure assessment guidance were used to develop candidate soil/dust source partition scenarios estimating lead intake, allowing estimation of age-specific soil/dust ingestion rates. These ingestion rate and bioavailability estimates were simultaneously applied to the U.S. EPA Integrated Exposure Uptake Biokinetic Model for Lead in Children to determine those combinations best approximating observed BLLs. Results: Absolute soil and house dust bioavailability averaged 33% (SD ± 4%) and 28% (SD ± 6%), respectively. Estimated BHSS age-specific soil/du
Improving estimates of tree mortality probability using potential growth rate
Das, Adrian J.; Stephenson, Nathan L.
2015-01-01
Tree growth rate is frequently used to estimate mortality probability. Yet, growth metrics can vary in form, and the justification for using one over another is rarely clear. We tested whether a growth index (GI) that scales the realized diameter growth rate against the potential diameter growth rate (PDGR) would give better estimates of mortality probability than other measures. We also tested whether PDGR, being a function of tree size, might better correlate with the baseline mortality probability than direct measurements of size such as diameter or basal area. Using a long-term dataset from the Sierra Nevada, California, U.S.A., as well as existing species-specific estimates of PDGR, we developed growth–mortality models for four common species. For three of the four species, models that included GI, PDGR, or a combination of GI and PDGR were substantially better than models without them. For the fourth species, the models including GI and PDGR performed roughly as well as a model that included only the diameter growth rate. Our results suggest that using PDGR can improve our ability to estimate tree survival probability. However, in the absence of PDGR estimates, the diameter growth rate was the best empirical predictor of mortality, in contrast to assumptions often made in the literature.
Estimation of transition probabilities of credit ratings for several companies
NASA Astrophysics Data System (ADS)
Peng, Gan Chew; Hin, Pooi Ah
2016-10-01
This paper attempts to estimate the transition probabilities of credit ratings for a number of companies whose ratings have a dependence structure. Binary codes are used to represent the index of a company together with its ratings in the present and next quarters. We initially fit the data on the vector of binary codes with a multivariate power-normal distribution. We next compute the multivariate conditional distribution for the binary codes of rating in the next quarter when the index of the company and binary codes of the company in the present quarter are given. From the conditional distribution, we compute the transition probabilities of the company's credit ratings in two consecutive quarters. The resulting transition probabilities tally fairly well with the maximum likelihood estimates for the time-independent transition probabilities.
Respiratory rate estimation during triage of children in hospitals.
Shah, Syed Ahmar; Fleming, Susannah; Thompson, Matthew; Tarassenko, Lionel
2015-01-01
Accurate assessment of a child's health is critical for appropriate allocation of medical resources and timely delivery of healthcare in Emergency Departments. The accurate measurement of vital signs is a key step in the determination of the severity of illness and respiratory rate is currently the most difficult vital sign to measure accurately. Several previous studies have attempted to extract respiratory rate from photoplethysmogram (PPG) recordings. However, the majority have been conducted in controlled settings using PPG recordings from healthy subjects. In many studies, manual selection of clean sections of PPG recordings was undertaken before assessing the accuracy of the signal processing algorithms developed. Such selection procedures are not appropriate in clinical settings. A major limitation of AR modelling, previously applied to respiratory rate estimation, is an appropriate selection of model order. This study developed a novel algorithm that automatically estimates respiratory rate from a median spectrum constructed applying multiple AR models to processed PPG segments acquired with pulse oximetry using a finger probe. Good-quality sections were identified using a dynamic template-matching technique to assess PPG signal quality. The algorithm was validated on 205 children presenting to the Emergency Department at the John Radcliffe Hospital, Oxford, UK, with reference respiratory rates up to 50 breaths per minute estimated by paediatric nurses. At the time of writing, the authors are not aware of any other study that has validated respiratory rate estimation using data collected from over 200 children in hospitals during routine triage.
Pottels Equation for Estimation of Glomerular Filtration Rate.
Barman, Himesh; Bisai, Samiran; Das, Bipul Kumar; Nath, Chandan Kumar; Duwarah, Sourabh Gohain
2017-01-15
The retrospective study was carried out to examine performance of Pottels height- independent equation compared to Schwartzs height-dependent equation to estimate glomerular filtration rate in 115 children in Indian setting. The Pottels equation performed well compared to updated Schwartz equation (R2=0.94, mean bias 0.25, 95% LOA=20.4, -19.9). The precision was better at lower range of estimated GFR.
Outcrossing rates and relatedness estimates in pecan (Carya illinoinensis) populations.
Rüter, B; Hamrick, J L; Wood, B W
2000-01-01
Estimates of single and multilocus outcrossing rates as well as relatedness among progeny of individual seed trees were obtained for 14 populations of pecan [Carya illinoinensis (Wangenh.) K. Koch]. Mean outcrossing estimates were not significantly different from 1.0 and relatedness values indicate that most progeny within families are half sibs. Biparental inbreeding was insignificant in all study sites, and inbreeding coefficients indicated that populations were close to inbreeding equilibrium.
Evaluating the performance of equations for estimating glomerular filtration rate.
Stevens, Lesley A; Zhang, Yaping; Schmid, Christopher H
2008-01-01
Glomerular filtration rate (GFR) is an important indicator of kidney function, critical for detection, evaluation and management of chronic kidney disease (CKD). GFR cannot be practically measured in most clinical or research settings; therefore, estimating equations are used as a primary measure of kidney function. A considerable body of literature now evaluates the performance of GFR estimating equations. The results of these studies are often not comparable, because of variation in GFR measurement methods, endogenous filtration marker assays and tools by which the equations were evaluated. In this article, methods for the evaluation of GFR estimating equations are discussed. Topics addressed include statistical methods used in development and validation of equations; explanation of measures of performance used for evaluation, with focus on distinction between bias, precision and accuracy, and with reference to examples of published evaluations of creatinine- and cystatin C-based equations; explanation of errors in GFR estimates; and challenges and questions in reporting performance of GFR estimating equations.
Automating proliferation rate estimation from Ki-67 histology images
NASA Astrophysics Data System (ADS)
Al-Lahham, Heba Z.; Alomari, Raja S.; Hiary, Hazem; Chaudhary, Vipin
2012-03-01
Breast cancer is the second cause of women death and the most diagnosed female cancer in the US. Proliferation rate estimation (PRE) is one of the prognostic indicators that guide the treatment protocols and it is clinically performed from Ki-67 histopathology images. Automating PRE substantially increases the efficiency of the pathologists. Moreover, presenting a deterministic and reproducible proliferation rate value is crucial to reduce inter-observer variability. To that end, we propose a fully automated CAD system for PRE from the Ki-67 histopathology images. This CAD system is based on a model of three steps: image pre-processing, image clustering, and nuclei segmentation and counting that are finally followed by PRE. The first step is based on customized color modification and color-space transformation. Then, image pixels are clustered by K-Means depending on the features extracted from the images derived from the first step. Finally, nuclei are segmented and counted using global thresholding, mathematical morphology and connected component analysis. Our experimental results on fifty Ki-67-stained histopathology images show a significant agreement between our CAD's automated PRE and the gold standard's one, where the latter is an average between two observers' estimates. The Paired T-Test, for the automated and manual estimates, shows ρ = 0.86, 0.45, 0.8 for the brown nuclei count, blue nuclei count, and proliferation rate, respectively. Thus, our proposed CAD system is as reliable as the pathologist estimating the proliferation rate. Yet, its estimate is reproducible.
The estimation of galactic cosmic ray penetration and dose rates
NASA Technical Reports Server (NTRS)
Burrell, M. O.; Wright, J. J.
1972-01-01
This study is concerned with approximation methods that can be readily applied to estimate the absorbed dose rate from cosmic rays in rads - tissue or rems inside simple geometries of aluminum. The present work is limited to finding the dose rate at the center of spherical shells or behind plane slabs. The dose rate is calculated at tissue-point detectors or for thin layers of tissue. This study considers cosmic-rays dose rates for both free-space and earth-orbiting missions.
Application of expert system to load composition rate estimation algorithm
Lim, J.Y.; Kim, J.H.; Kim, J.O.; Singh, C.
1999-08-01
A load model is needed for the power system analysis including load flow and stability studies. The load model representation needs the load composition rate indicating the portion of several typical load groups. This paper proposes a refined load composition rate estimation algorithm with input data which are relative coefficients, limit values, and the energy portion of load groups. An expert system is constructed with the consideration of the uncertainty of input data. The load composition rates in several customers in the power system are obtained and the results of case studies show that a reasonable load composition rate is achieved.
Spiking neural networks for cortical neuronal spike train decoding.
Fang, Huijuan; Wang, Yongji; He, Jiping
2010-04-01
Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the timing of spikes, are a better alternative to the coding scheme based on spike frequency (histogram) alone. The SNN model analyzes cortical neural spike trains directly without losing temporal information for generating more reliable motor command for cortically controlled prosthetics. In this letter, we compared the temporal pattern classification result from the SNN approach with results generated from firing-rate-based approaches: conventional artificial neural networks, support vector machines, and linear regression. The results show that the SNN algorithm can achieve higher classification accuracy and identify the spiking activity related to movement control earlier than the other methods. Both are desirable characteristics for fast neural information processing and reliable control command pattern recognition for neuroprosthetic applications.
Magnetometer-Only Attitude and Rate Estimates for Spinning Spacecraft
NASA Technical Reports Server (NTRS)
Challa, M.; Natanson, G.; Ottenstein, N.
2000-01-01
A deterministic algorithm and a Kalman filter for gyroless spacecraft are used independently to estimate the three-axis attitude and rates of rapidly spinning spacecraft using only magnetometer data. In-flight data from the Wide-Field Infrared Explorer (WIRE) during its tumble, and the Fast Auroral Snapshot Explorer (FAST) during its nominal mission mode are used to show that the algorithms can successfully estimate the above in spite of the high rates. Results using simulated data are used to illustrate the importance of accurate and frequent data.
Probability model for estimating colorectal polyp progression rates.
Gopalappa, Chaitra; Aydogan-Cremaschi, Selen; Das, Tapas K; Orcun, Seza
2011-03-01
According to the American Cancer Society, colorectal cancer (CRC) is the third most common cause of cancer related deaths in the United States. Experts estimate that about 85% of CRCs begin as precancerous polyps, early detection and treatment of which can significantly reduce the risk of CRC. Hence, it is imperative to develop population-wide intervention strategies for early detection of polyps. Development of such strategies requires precise values of population-specific rates of incidence of polyp and its progression to cancerous stage. There has been a considerable amount of research in recent years on developing screening based CRC intervention strategies. However, these are not supported by population-specific mathematical estimates of progression rates. This paper addresses this need by developing a probability model that estimates polyp progression rates considering race and family history of CRC; note that, it is ethically infeasible to obtain polyp progression rates through clinical trials. We use the estimated rates to simulate the progression of polyps in the population of the State of Indiana, and also the population of a clinical trial conducted in the State of Minnesota, which was obtained from literature. The results from the simulations are used to validate the probability model.
Robust spike-train learning in spike-event based weight update.
Shrestha, Sumit Bam; Song, Qing
2017-09-12
Supervised learning algorithms in a spiking neural network either learn a spike-train pattern for a single neuron receiving input spike-train from multiple input synapses or learn to output the first spike time in a feedforward network setting. In this paper, we build upon spike-event based weight update strategy to learn continuous spike-train in a spiking neural network with a hidden layer using a dead zone on-off based adaptive learning rate rule which ensures convergence of the learning process in the sense of weight convergence and robustness of the learning process to external disturbances. Based on different benchmark problems, we compare this new method with other relevant spike-train learning algorithms. The results show that the speed of learning is much improved and the rate of successful learning is also greatly improved. Copyright © 2017 Elsevier Ltd. All rights reserved.
A New Approach for Estimating Entrainment Rate in Cumulus Clouds
Lu C.; Liu, Y.; Yum, S. S.; Niu, S.; Endo, S.
2012-02-16
A new approach is presented to estimate entrainment rate in cumulus clouds. The new approach is directly derived from the definition of fractional entrainment rate and relates it to mixing fraction and the height above cloud base. The results derived from the new approach compare favorably with those obtained with a commonly used approach, and have smaller uncertainty. This new approach has several advantages: it eliminates the need for in-cloud measurements of temperature and water vapor content, which are often problematic in current aircraft observations; it has the potential for straightforwardly connecting the estimation of entrainment rate and the microphysical effects of entrainment-mixing processes; it also has the potential for developing a remote sensing technique to infer entrainment rate.
Estimating 1 min rain rate distributions from numerical weather prediction
NASA Astrophysics Data System (ADS)
Paulson, Kevin S.
2017-01-01
Internationally recognized prognostic models of rain fade on terrestrial and Earth-space EHF links rely fundamentally on distributions of 1 min rain rates. Currently, in Rec. ITU-R P.837-6, these distributions are generated using the Salonen-Poiares Baptista method where 1 min rain rate distributions are estimated from long-term average annual accumulations provided by numerical weather prediction (NWP). This paper investigates an alternative to this method based on the distribution of 6 h accumulations available from the same NWPs. Rain rate fields covering the UK, produced by the Nimrod network of radars, are integrated to estimate the accumulations provided by NWP, and these are linked to distributions of fine-scale rain rates. The proposed method makes better use of the available data. It is verified on 15 NWP regions spanning the UK, and the extension to other regions is discussed.
Estimating Source Recurrence Rates for Probabilistic Tsunami Hazard Analysis (PTHA)
NASA Astrophysics Data System (ADS)
Geist, E. L.; Parsons, T.
2004-12-01
A critical factor in probabilistic tsunami hazard analysis (PTHA) is estimating the average recurrence rate for tsunamigenic sources. Computational PTHA involves aggregating runup values derived from numerical simulations for many far-field and local sources, primarily earthquakes, each with a specified probability of occurrence. Computational PTHA is the primary method used in the ongoing FEMA pilot study at Seaside, Oregon. For a Poissonian arrival time model, the probability for a given source is dependent on a single parameter: the mean inter-event time of the source. In other probability models, parameters such as aperiodicity are also included. In this study, we focus on methods to determine the recurrence rates for large, shallow subduction zone earthquakes. For earthquakes below about M=8, recurrence rates can be obtained from modified Gutenberg-Richter distributions that are constrained by the tectonic moment rate for individual subduction zones. However, significant runup from far-field sources is commonly associated with the largest magnitude earthquakes, for which the recurrence rates are poorly constrained by the tail of empirical frequency-magnitude relationships. For these earthquakes, paleoseismic evidence of great earthquakes can be used to establish recurrence rates. Because the number of geologic horizons representing great earthquakes along a particular subduction zone is limited, special techniques are needed to account for open intervals before the first and after the last observed events. Uncertainty in age dates for the horizons also has to be included in estimating recurrence rates and aperiodicity. A Monte Carlo simulation is performed in which a random sample of earthquake times is drawn from a specified probability distribution with varying average recurrence rates and aperiodicities. A recurrence rate can be determined from the mean rate of all random samples that fit the observations, or a range of rates can be carried through the
Penetrometry and estimation of the flow rate of powder excipients.
Zatloukal, Z; Sklubalová, Z
2007-03-01
In this work, penetrometry with a sphere was employed to study the flow properties of non-consolidated pharmaceutical powder excipients: sodium chloride, sodium citrate, boric acid, and sorbitol. In order to estimate flow rate, the pressure of penetration in Pascals was used. Penetrometry measurement with a sphere requires modification of the measurement container, in particular by decreasing the diameter of the container, to prevent undesirable movement of material in a direction opposite to that in which the sphere penetrates. Thus penetrometry by a sphere seems to be similar to indentation by the Brinell hardness tester. The pressure of penetration was determined from the depth of penetration by analogy with the Brinell hardness number and an equation for the inter conversion of the two variables is presented. The penetration pressure allowed direct estimation of the flow rate only for those powder excipients with a size fraction in the range of 0.250-0.630 mm. Using the ratio of penetration pressure to bulk density, a polynomial quadratic equation was generated from which the flow rates for the group of all tested powders could be estimated. Finally, if the inverse ratio of bulk density and penetration pressure was used as an independent variable, the flow rate could be estimated by linear regression with the coefficient of determination r2 = 0.9941. In conclusion, using sphere penetrometry, the flow properties of non-consolidated powder samples could be investigated by indentation. As a result, a linear regression in which the flow rate was directly proportional to the powder bulk density and inversely proportional to the penetration pressure could be best recommended for the estimation of the flow rate of powder excipients.
Burroughs, Amelia; Wise, Andrew K.; Xiao, Jianqiang; Houghton, Conor; Tang, Tianyu; Suh, Colleen Y.; Lang, Eric J.
2016-01-01
Key points Purkinje cells are the sole output of the cerebellar cortex and fire two distinct types of action potential: simple spikes and complex spikes.Previous studies have mainly considered complex spikes as unitary events, even though the waveform is composed of varying numbers of spikelets.The extent to which differences in spikelet number affect simple spike activity (and vice versa) remains unclear.We found that complex spikes with greater numbers of spikelets are preceded by higher simple spike firing rates but, following the complex spike, simple spikes are reduced in a manner that is graded with spikelet number.This dynamic interaction has important implications for cerebellar information processing, and suggests that complex spike spikelet number may maintain Purkinje cells within their operational range. Abstract Purkinje cells are central to cerebellar function because they form the sole output of the cerebellar cortex. They exhibit two distinct types of action potential: simple spikes and complex spikes. It is widely accepted that interaction between these two types of impulse is central to cerebellar cortical information processing. Previous investigations of the interactions between simple spikes and complex spikes have mainly considered complex spikes as unitary events. However, complex spikes are composed of an initial large spike followed by a number of secondary components, termed spikelets. The number of spikelets within individual complex spikes is highly variable and the extent to which differences in complex spike spikelet number affects simple spike activity (and vice versa) remains poorly understood. In anaesthetized adult rats, we have found that Purkinje cells recorded from the posterior lobe vermis and hemisphere have high simple spike firing frequencies that precede complex spikes with greater numbers of spikelets. This finding was also evident in a small sample of Purkinje cells recorded from the posterior lobe hemisphere in awake
Anthropogenic radioisotopes to estimate rates of soil redistribution by wind
USDA-ARS?s Scientific Manuscript database
Erosion of soil by wind and water is a degrading process that affects millions of hectares worldwide. Atmospheric testing of nuclear weapons and the resulting fallout of anthropogenic radioisotopes, particularly Cesium 137, has made possible the estimation of mean soil redistribution rates. The pe...
Anthropogenic radionuclides for estimating rates of soil redistribution by wind
USDA-ARS?s Scientific Manuscript database
Erosion of soil by wind and water is a degrading process that affects millions of hectares worldwide. Atmospheric testing of nuclear weapons and the resulting fallout of anthropogenic radioisotopes, particularly Cesium 137, has made possible the estimation of mean soil redistribution rates. The pe...
Lidar method to estimate emission rates from extended sources
USDA-ARS?s Scientific Manuscript database
Currently, point measurements, often combined with models, are the primary means by which atmospheric emission rates are estimated from extended sources. However, these methods often fall short in their spatial and temporal resolution and accuracy. In recent years, lidar has emerged as a suitable to...
Estimating sedimentation from an erosion-hazard rating
R. M. Rice; S. A. Sherbin
1977-01-01
Data from two watersheds in northern California were used to develop an interpretation of the erosion-hazard rating (EHR) of the Coast Forest District as amount of sedimentation. For the Caspar Creek Experimental Watershed (North Fork and South Fork), each EHR unit was estimated as equivalent to 0.0543 cubic yards per acre per year, on undisturbed forest. Experience...
Optical range and range rate estimation for teleoperator systems
NASA Technical Reports Server (NTRS)
Shields, N. L., Jr.; Kirkpatrick, M., III; Malone, T. B.; Huggins, C. T.
1974-01-01
Range and range rate are crucial parameters which must be available to the operator during remote controlled orbital docking operations. A method was developed for the estimation of both these parameters using an aided television system. An experiment was performed to determine the human operator's capability to measure displayed image size using a fixed reticle or movable cursor as the television aid. The movable cursor was found to yield mean image size estimation errors on the order of 2.3 per cent of the correct value. This error rate was significantly lower than that for the fixed reticle. Performance using the movable cursor was found to be less sensitive to signal-to-noise ratio variation than was that for the fixed reticle. The mean image size estimation errors for the movable cursor correspond to an error of approximately 2.25 per cent in range suggesting that the system has some merit. Determining the accuracy of range rate estimation using a rate controlled cursor will require further experimentation.
Optical range and range rate estimation for teleoperator systems
NASA Technical Reports Server (NTRS)
Shields, N. L., Jr.; Kirkpatrick, M., III; Malone, T. B.; Huggins, C. T.
1974-01-01
Range and range rate are crucial parameters which must be available to the operator during remote controlled orbital docking operations. A method was developed for the estimation of both these parameters using an aided television system. An experiment was performed to determine the human operator's capability to measure displayed image size using a fixed reticle or movable cursor as the television aid. The movable cursor was found to yield mean image size estimation errors on the order of 2.3 per cent of the correct value. This error rate was significantly lower than that for the fixed reticle. Performance using the movable cursor was found to be less sensitive to signal-to-noise ratio variation than was that for the fixed reticle. The mean image size estimation errors for the movable cursor correspond to an error of approximately 2.25 per cent in range suggesting that the system has some merit. Determining the accuracy of range rate estimation using a rate controlled cursor will require further experimentation.
Estimating Sedimentation from an Erosion-Hazard Rating
R.M. Rice; S.A. Sherbin
1977-01-01
Data from two watersheds in northern California were used to develop an interpretation of the erosion hazard rating (EHR) of the Coast Forest District as amount of sedimentation. For the Caspar Creek Experimental Watershed (North Fork and South Fork), each EHR unit was estimated as equivalent to 0.0543 cubic yards per acre per year, on undisturbed forest. Experience...
Robust estimation of fetal heart rate variability using Doppler ultrasound.
Fernando, Kumari L; Mathews, V John; Varner, Michael W; Clark, Edward B
2003-08-01
This paper presents a new measure of heart rate variability (HRV) that can be estimated using Doppler ultrasound techniques and is robust to variations in the angle of incidence of the ultrasound beam and the measurement noise. This measure employs the multiple signal characterization (MUSIC) algorithm which is a high-resolution method for estimating the frequencies of sinusoidal signals embedded in white noise from short-duration measurements. We show that the product of the square-root of the estimated signal-to-noise ratio (SNR) and the mean-square error of the frequency estimates is independent of the noise level in the signal. Since varying angles of incidence effectively changes the input SNR, this measure of HRV is robust to the input noise as well as the angle of incidence. This paper includes the results of analyzing synthetic and real Doppler ultrasound data that demonstrates the usefulness of the new measure in HRV analysis.
A Spiking Neural Network in sEMG Feature Extraction
Lobov, Sergey; Mironov, Vasiliy; Kastalskiy, Innokentiy; Kazantsev, Victor
2015-01-01
We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demonstrate that the classification accuracy of the proposed model could reach high values comparable with existing sEMG interface systems. Moreover, the algorithm sensibility for different sEMG collecting systems characteristics was estimated. Results showed rather equal accuracy, despite a significant sampling rate difference. The proposed algorithm was successfully tested for mobile robot control. PMID:26540060
Estimating the Rate of Retinal Ganglion Cell Loss in Glaucoma
Medeiros, Felipe A.; Zangwill, Linda M.; Anderson, Douglas R.; Liebmann, Jeffrey M.; Girkin, Christopher A; Harwerth, Ronald S.; Fredette, Marie-Josée; Weinreb, Robert N.
2013-01-01
Purpose To present and evaluate a new method of estimating rates of retinal ganglion cell (RGC) loss in glaucoma by combining structural and functional measurements. Design Observational cohort study Methods The study included 213 eyes of 213 glaucoma patients followed for an average of 4.5±0.8 years with standard automated perimetry (SAP) visual fields and optical coherence tomography (OCT). A control group of 33 eyes of 33 glaucoma patients had repeated tests over a short period of time to test the specificity of the method. An additional group of 52 eyes from 52 healthy subjects followed for an average of 4.0±0.7 years was used to estimate age-related losses of RGCs. Estimates of RGC counts were obtained from SAP and OCT and a weighted average was used to obtain a final estimate of the number of RGCs for each eye. The rate of RGC loss was calculated for each eye using linear regression. Progression was defined by a statistically significant slope faster than the age-expected loss of RGCs. Results From the 213 eyes, 47 (22.1%) showed rates of RGC loss that were faster than the age-expected decline. A larger proportion of glaucomatous eyes showed progression based on rates of RGC loss than based on isolated parameters from SAP (8.5%) or OCT (14.6%; P<0.01), while maintaining similar specificities in the stable group. Conclusion The rate of RGC loss estimated from combining structure and function performed better than either isolated structural or functional measures for detecting progressive glaucomatous damage. PMID:22840484
Updated Magmatic Flux Rate Estimates for the Hawaii Plume
NASA Astrophysics Data System (ADS)
Wessel, P.
2013-12-01
Several studies have estimated the magmatic flux rate along the Hawaiian-Emperor Chain using a variety of methods and arriving at different results. These flux rate estimates have weaknesses because of incomplete data sets and different modeling assumptions, especially for the youngest portion of the chain (<3 Ma). While they generally agree on the 1st order features, there is less agreement on the magnitude and relative size of secondary flux variations. Some of these differences arise from the use of different methodologies, but the significance of this variability is difficult to assess due to a lack of confidence bounds on the estimates obtained with these disparate methods. All methods introduce some error, but to date there has been little or no quantification of error estimates for the inferred melt flux, making an assessment problematic. Here we re-evaluate the melt flux for the Hawaii plume with the latest gridded data sets (SRTM30+ and FAA 21.1) using several methods, including the optimal robust separator (ORS) and directional median filtering techniques (DiM). We also compute realistic confidence limits on the results. In particular, the DiM technique was specifically developed to aid in the estimation of surface loads that are superimposed on wider bathymetric swells and it provides error estimates on the optimal residuals. Confidence bounds are assigned separately for the estimated surface load (obtained from the ORS regional/residual separation techniques) and the inferred subsurface volume (from gravity-constrained isostasy and plate flexure optimizations). These new and robust estimates will allow us to assess which secondary features in the resulting melt flux curve are significant and should be incorporated when correlating melt flux variations with other geophysical and geochemical observations.
A Pulse Rate Estimation Algorithm Using PPG and Smartphone Camera.
Siddiqui, Sarah Ali; Zhang, Yuan; Feng, Zhiquan; Kos, Anton
2016-05-01
The ubiquitous use and advancement in built-in smartphone sensors and the development in big data processing have been beneficial in several fields including healthcare. Among the basic vitals monitoring, pulse rate monitoring is the most important healthcare necessity. A multimedia video stream data acquired by built-in smartphone camera can be used to estimate it. In this paper, an algorithm that uses only smartphone camera as a sensor to estimate pulse rate using PhotoPlethysmograph (PPG) signals is proposed. The results obtained by the proposed algorithm are compared with the actual pulse rate and the maximum error found is 3 beats per minute. The standard deviation in percentage error and percentage accuracy is found to be 0.68 % whereas the average percentage error and percentage accuracy is found to be 1.98 % and 98.02 % respectively.
Estimating uplift rate histories from river profiles using African examples
NASA Astrophysics Data System (ADS)
Roberts, Gareth G.; White, Nicky
2010-02-01
We describe and apply a method for estimating uplift rate histories from longitudinal river profiles. Our strategy is divided into three parts. First, we develop a forward model, which calculates river profiles from uplift rate histories. Height variation along a river profile is controlled by uplift rate and moderated by the erosional process. We assume that the erosional process can be represented by a combination of advection and diffusion, which are parameterized using four erosional constants. Second, we have posed and solved the geologically more interesting inverse problem: which uplift rate history minimizes the misfit between calculated and observed river profiles? The inverse algorithm has been tested on synthetic river profiles, which demonstrates that uplift rate histories can be reliably retrieved. Our tests show that the erosional process is dominated by advection (i.e., knickpoint retreat) and that changes in lithology and discharge play a secondary role in determining the transient form of a river profile. Finally, we have inverted river profiles from a series of African topographic swells, namely the Bié, South African, Namibian, Hoggar, and Tibesti domes. Fits between calculated and observed river profiles are excellent. Calculated uplift rate histories suggest that these domes grew rapidly during the last 30-40 million years. Uplift rate histories vary significantly from dome to dome but cumulative uplift histories agree closely with independent geologic estimates.
Estimates of Biogenic Methane Production Rates in Deep Marine Sediments
NASA Astrophysics Data System (ADS)
Colwell, F. S.; Boyd, S.; Delwiche, M. E.; Reed, D. W.
2004-12-01
Much of the methane in natural gas hydrates in marine sediments is made by methanogens. Current models used to predict hydrate distribution and concentration in these sediments require estimates of microbial methane production rates. However, accurate estimates are difficult to achieve because of the bias introduced by sampling and because methanogen activities in these sediments are low and not easily detected. To derive useful methane production rates for marine sediments we have measured the methanogen biomass in samples taken from different depths in Hydrate Ridge (HR) sediments off the coast of Oregon and, separately, the minimal rates of activity for a methanogen in a laboratory reactor. For methanogen biomass, we used a polymerase chain reaction assay in real time to target the methanogen-specific mcr gene. Using this method we found that a majority of the samples collected from boreholes at HR show no evidence of methanogens (detection limit: less than 100 methanogens per g of sediment). Most of the samples with detectable numbers of methanogens were from shallow sediments (less than 10 meters below seafloor [mbsf]) although a few samples with apparently high numbers of methanogens (greater than 10,000 methanogens per g) were from as deep as 230 mbsf and were associated with notable geological features (e.g., the bottom-simulating reflector and an ash-bearing zone with high fluid movement). Laboratory studies with Methanoculleus submarinus (isolated from a hydrate zone at the Nankai Trough) maintained in a biomass recycle reactor showed that when this methanogen is merely surviving, as is likely the case in deep marine sediments, it produces approximately 0.06 fmol methane per cell per day. This is far lower than rates reported for methanogens in other environments. By combining this estimate of specific methanogenic rates and an extrapolation from the numbers of methanogens at selected depths in the sediment column at HR sites we have derived a maximum
Induced abortion in Tehran, Iran: estimated rates and correlates.
Erfani, Amir
2011-09-01
Abortion is severely restricted in Iran, and many women with an unwanted pregnancy resort to clandes-tine, unsafe abortions. Accurate information on abortion incidence is needed to assess the extent to which women ?experience unwanted pregnancies and to allocate resources for contraceptive services. Data for analysis came from 2,934 married women aged 15-49 who completed the 2009 Tehran Survey of Fertility. Estimated abortion rates and proportions of known pregnancies that end in abortion were calculated for all women and for demographic and socioeconomic subgroups, and descriptive data were used to examine women's contraceptive use and reasons for having an abortion. Annually, married women in Tehran have about 11,500 abortions. In the year before the survey, the estimated total abortion rate was 0.16 abortions per woman, and the annual general abortion rate was 5.5 abortions per 1,000 women; the general abortion rate peaked at 11.7 abortions among those aged 30-34. An estimated 8.7 of every 100 known pregnancies ended in abortion. The abortion rate was elevated among women who were employed or had high levels of income or education, as well as among those who reported a low level of religiosity, had two children or wanted no more. Fertility-related and socioeconomic reasons were cited by seven in 10 women who obtained an abortion. More than two-thirds of pregnancies that were terminated resulted from method failures among women who had used withdrawal, the pill or a condom. Estimated abortion rates and their correlates can help policymakers and program planners identify subgroups of women who are in particular need of services and counseling to prevent unwanted pregnancy.
Rating curve estimation of nutrient loads in Iowa rivers
Stenback, G.A.; Crumpton, W.G.; Schilling, K.E.; Helmers, M.J.
2011-01-01
Accurate estimation of nutrient loads in rivers and streams is critical for many applications including determination of sources of nutrient loads in watersheds, evaluating long-term trends in loads, and estimating loading to downstream waterbodies. Since in many cases nutrient concentrations are measured on a weekly or monthly frequency, there is a need to estimate concentration and loads during periods when no data is available. The objectives of this study were to: (i) document the performance of a multiple regression model to predict loads of nitrate and total phosphorus (TP) in Iowa rivers and streams; (ii) determine whether there is any systematic bias in the load prediction estimates for nitrate and TP; and (iii) evaluate streamflow and concentration factors that could affect the load prediction efficiency. A commonly cited rating curve regression is utilized to estimate riverine nitrate and TP loads for rivers in Iowa with watershed areas ranging from 17.4 to over 34,600km2. Forty-nine nitrate and 44 TP datasets each comprising 5-22years of approximately weekly to monthly concentrations were examined. Three nitrate data sets had sample collection frequencies averaging about three samples per week. The accuracy and precision of annual and long term riverine load prediction was assessed by direct comparison of rating curve load predictions with observed daily loads. Significant positive bias of annual and long term nitrate loads was detected. Long term rating curve nitrate load predictions exceeded observed loads by 25% or more at 33% of the 49 measurement sites. No bias was found for TP load prediction although 15% of the 44 cases either underestimated or overestimate observed long-term loads by more than 25%. The rating curve was found to poorly characterize nitrate and phosphorus variation in some rivers. ?? 2010 .
The relationship between seizures, interictal spikes and antiepileptic drugs.
Goncharova, Irina I; Alkawadri, Rafeed; Gaspard, Nicolas; Duckrow, Robert B; Spencer, Dennis D; Hirsch, Lawrence J; Spencer, Susan S; Zaveri, Hitten P
2016-09-01
A considerable decrease in spike rate accompanies antiepileptic drug (AED) taper during intracranial EEG (icEEG) monitoring. Since spike rate during icEEG monitoring can be influenced by surgery to place intracranial electrodes, we studied spike rate during long-term scalp EEG monitoring to further test this observation. We analyzed spike rate, seizure occurrence and AED taper in 130 consecutive patients over an average of 8.9days (range 5-17days). We observed a significant relationship between time to the first seizure, spike rate, AED taper and seizure occurrence (F (3,126)=19.77, p<0.0001). A high spike rate was related to a longer time to the first seizure. Further, in a subset of 79 patients who experienced seizures on or after day 4 of monitoring, spike rate decreased initially from an on- to off-AEDs epoch (from 505.0 to 382.3 spikes per hour, p<0.00001), and increased thereafter with the occurrence of seizures. There is an interplay between seizures, spikes and AEDs such that spike rate decreases with AED taper and increases after seizure occurrence. The direct relationship between spike rate and AEDs and between spike rate and time to the first seizure suggests that spikes are a marker of inhibition rather than excitation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Quantifying Spike Train Oscillations: Biases, Distortions and Solutions
Matzner, Ayala; Bar-Gad, Izhar
2015-01-01
Estimation of the power spectrum is a common method for identifying oscillatory changes in neuronal activity. However, the stochastic nature of neuronal activity leads to severe biases in the estimation of these oscillations in single unit spike trains. Different biological and experimental factors cause the spike train to differentially reflect its underlying oscillatory rate function. We analyzed the effect of factors, such as the mean firing rate and the recording duration, on the detectability of oscillations and their significance, and tested these theoretical results on experimental data recorded in Parkinsonian non-human primates. The effect of these factors is dramatic, such that in some conditions, the detection of existing oscillations is impossible. Moreover, these biases impede the comparison of oscillations across brain regions, neuronal types, behavioral states and separate recordings with different underlying parameters, and lead inevitably to a gross misinterpretation of experimental results. We introduce a novel objective measure, the "modulation index", which overcomes these biases, and enables reliable detection of oscillations from spike trains and a direct estimation of the oscillation magnitude. The modulation index detects a high percentage of oscillations over a wide range of parameters, compared to classical spectral analysis methods, and enables an unbiased comparison between spike trains recorded from different neurons and using different experimental protocols. PMID:25909328
Shimazaki, Hideaki; Amari, Shun-Ichi; Brown, Emery N; Grün, Sonja
2012-01-01
Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods
Improved Versions of Common Estimators of the Recombination Rate.
Gärtner, Kerstin; Futschik, Andreas
2016-09-01
The scaled recombination parameter [Formula: see text] is one of the key parameters, turning up frequently in population genetic models. Accurate estimates of [Formula: see text] are difficult to obtain, as recombination events do not always leave traces in the data. One of the most widely used approaches is composite likelihood. Here, we show that popular implementations of composite likelihood estimators can often be uniformly improved by optimizing the trade-off between bias and variance. The amount of possible improvement depends on parameters such as the sequence length, the sample size, and the mutation rate, and it can be considerable in some cases. It turns out that approximate Bayesian computation, with composite likelihood as a summary statistic, also leads to improved estimates, but now in terms of the posterior risk. Finally, we demonstrate a practical application on real data from Drosophila.
Robust estimation of fetal heart rate from US Doppler signals
NASA Astrophysics Data System (ADS)
Voicu, Iulian; Girault, Jean-Marc; Roussel, Catherine; Decock, Aliette; Kouame, Denis
2010-01-01
Introduction: In utero, Monitoring of fetal wellbeing or suffering is today an open challenge, due to the high number of clinical parameters to be considered. An automatic monitoring of fetal activity, dedicated for quantifying fetal wellbeing, becomes necessary. For this purpose and in a view to supply an alternative for the Manning test, we used an ultrasound multitransducer multigate Doppler system. One important issue (and first step in our investigation) is the accurate estimation of fetal heart rate (FHR). An estimation of the FHR is obtained by evaluating the autocorrelation function of the Doppler signals for ills and healthiness foetus. However, this estimator is not enough robust since about 20% of FHR are not detected in comparison to a reference system. These non detections are principally due to the fact that the Doppler signal generated by the fetal moving is strongly disturbed by the presence of others several Doppler sources (mother' s moving, pseudo breathing, etc.). By modifying the existing method (autocorrelation method) and by proposing new time and frequency estimators used in the audio' s domain, we reduce to 5% the probability of non-detection of the fetal heart rate. These results are really encouraging and they enable us to plan the use of automatic classification techniques in order to discriminate between healthy and in suffering foetus.
Improved bit error rate estimation over experimental optical wireless channels
NASA Astrophysics Data System (ADS)
El Tabach, Mamdouh; Saoudi, Samir; Tortelier, Patrick; Bouchet, Olivier; Pyndiah, Ramesh
2009-02-01
As a part of the EU-FP7 R&D programme, the OMEGA project (hOME Gigabit Access) aims at bridging the gap between wireless terminals and wired backbone network in homes, providing high bit rate connectivity to users. Beside radio frequencies, the wireless links will use Optical Wireless (OW) communications. To guarantee high performance and quality of service in real-time, our system needs techniques to approximate the Bit Error Probability (BEP) with a reasonable training sequence. Traditionally, the BEP is approximated by the Bit Error Rate (BER) measured by counting the number of errors within a given sequence of bits. For small BERs, required sequences are huge and may prevent real-time estimation. In this paper, methods to estimate BER using Probability Density Function (PDF) estimation are presented. Two a posteriori techniques based on Parzen estimator or constrained Gram-Charlier series expansion are adapted and applied to OW communications. Aided by simulations, comparison is done over experimental optical channels. We show that, for different scenarios, such as optical multipath distortion or a well designed Code Division Multiple Access (CDMA) system, this approach outperforms the counting method and yields to better results with a relatively small training sequence.
Estimating river discharge rates through remotely sensed thermal plumes
NASA Astrophysics Data System (ADS)
Abou Najm, M.; Alameddine, I.; Ibrahim, E.; Nasr, R.
2016-12-01
An empirical relationship is developed for estimating river discharge rates from remotely sensed thermal plumes that generate due to the temperature gradient at the interface between rivers and large water bodies. The method first determines the plumes' near field area, length scale, and length scale deviation angle from river channel centerline from Landsat 7 ETM+ satellite images. It also makes use of mean river and ocean temperatures and tidal levels collected from NOAA. A multiple linear regression model is then used to predict measured daily discharge rates with the determined predictors. The approach is tested and validated with discharge rates collected from four USGS gauged rivers in Oregon and California. Results from 116 Landsat 7 ETM+ satellites images of the four rivers show that the standard error of the discharge estimates were within a factor of 1.5-2.0 of observed values, with mean estimate accuracy of 10%. Goodness of fit (R2) ranged from 0.51 for the Rogue River up to 0.64 for the Coquille and Siuslaw rivers. The method offers an opportunity to monitor changes in flow discharge in ungauged basins, where tidal flow is not dominating and where a temperature difference of 2 oC exists between the river and the receiving water body.
Estimating Divergence Times and Substitution Rates in Rhizobia
Chriki-Adeeb, Rim; Chriki, Ali
2016-01-01
Accurate estimation of divergence times of soil bacteria that form nitrogen-fixing associations with most leguminous plants is challenging because of a limited fossil record and complexities associated with molecular clocks and phylogenetic diversity of root nodule bacteria, collectively called rhizobia. To overcome the lack of fossil record in bacteria, divergence times of host legumes were used to calibrate molecular clocks and perform phylogenetic analyses in rhizobia. The 16S rRNA gene and intergenic spacer region remain among the favored molecular markers to reconstruct the timescale of rhizobia. We evaluate the performance of the random local clock model and the classical uncorrelated lognormal relaxed clock model, in combination with four tree models (coalescent constant size, birth–death, birth–death incomplete sampling, and Yule processes) on rhizobial divergence time estimates. Bayes factor tests based on the marginal likelihoods estimated from the stepping-stone sampling analyses strongly favored the random local clock model in combination with Yule process. Our results on the divergence time estimation from 16S rRNA gene and intergenic spacer region sequences are compatible with age estimates based on the conserved core genes but significantly older than those obtained from symbiotic genes, such as nodIJ genes. This difference may be due to the accelerated evolutionary rates of symbiotic genes compared to those of other genomic regions not directly implicated in nodulation processes. PMID:27168719
Probabilistic estimation of residential air exchange rates for ...
Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER measurements. An algorithm for probabilistically estimating AER was developed based on the Lawrence Berkley National Laboratory Infiltration model utilizing housing characteristics and meteorological data with adjustment for window opening behavior. The algorithm was evaluated by comparing modeled and measured AERs in four US cities (Los Angeles, CA; Detroit, MI; Elizabeth, NJ; and Houston, TX) inputting study-specific data. The impact on the modeled AER of using publically available housing data representative of the region for each city was also assessed. Finally, modeled AER based on region-specific inputs was compared with those estimated using literature-based distributions. While modeled AERs were similar in magnitude to the measured AER they were consistently lower for all cities except Houston. AERs estimated using region-specific inputs were lower than those using study-specific inputs due to differences in window opening probabilities. The algorithm produced more spatially and temporally variable AERs compared with literature-based distributions reflecting within- and between-city differences, helping reduce error in estimates of air pollutant exposure. Published in the Journal of
Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics
2016-01-01
Interpreting the potentially vast number of hypotheses generated by a shotgun proteomics experiment requires a valid and accurate procedure for assigning statistical confidence estimates to identified tandem mass spectra. Despite the crucial role such procedures play in most high-throughput proteomics experiments, the scientific literature has not reached a consensus about the best confidence estimation methodology. In this work, we evaluate, using theoretical and empirical analysis, four previously proposed protocols for estimating the false discovery rate (FDR) associated with a set of identified tandem mass spectra: two variants of the target-decoy competition protocol (TDC) of Elias and Gygi and two variants of the separate target-decoy search protocol of Käll et al. Our analysis reveals significant biases in the two separate target-decoy search protocols. Moreover, the one TDC protocol that provides an unbiased FDR estimate among the target PSMs does so at the cost of forfeiting a random subset of high-scoring spectrum identifications. We therefore propose the mix-max procedure to provide unbiased, accurate FDR estimates in the presence of well-calibrated scores. The method avoids biases associated with the two separate target-decoy search protocols and also avoids the propensity for target-decoy competition to discard a random subset of high-scoring target identifications. PMID:26152888
Estimating Divergence Times and Substitution Rates in Rhizobia.
Chriki-Adeeb, Rim; Chriki, Ali
2016-01-01
Accurate estimation of divergence times of soil bacteria that form nitrogen-fixing associations with most leguminous plants is challenging because of a limited fossil record and complexities associated with molecular clocks and phylogenetic diversity of root nodule bacteria, collectively called rhizobia. To overcome the lack of fossil record in bacteria, divergence times of host legumes were used to calibrate molecular clocks and perform phylogenetic analyses in rhizobia. The 16S rRNA gene and intergenic spacer region remain among the favored molecular markers to reconstruct the timescale of rhizobia. We evaluate the performance of the random local clock model and the classical uncorrelated lognormal relaxed clock model, in combination with four tree models (coalescent constant size, birth-death, birth-death incomplete sampling, and Yule processes) on rhizobial divergence time estimates. Bayes factor tests based on the marginal likelihoods estimated from the stepping-stone sampling analyses strongly favored the random local clock model in combination with Yule process. Our results on the divergence time estimation from 16S rRNA gene and intergenic spacer region sequences are compatible with age estimates based on the conserved core genes but significantly older than those obtained from symbiotic genes, such as nodIJ genes. This difference may be due to the accelerated evolutionary rates of symbiotic genes compared to those of other genomic regions not directly implicated in nodulation processes.
Probabilistic estimation of residential air exchange rates for ...
Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER measurements. An algorithm for probabilistically estimating AER was developed based on the Lawrence Berkley National Laboratory Infiltration model utilizing housing characteristics and meteorological data with adjustment for window opening behavior. The algorithm was evaluated by comparing modeled and measured AERs in four US cities (Los Angeles, CA; Detroit, MI; Elizabeth, NJ; and Houston, TX) inputting study-specific data. The impact on the modeled AER of using publically available housing data representative of the region for each city was also assessed. Finally, modeled AER based on region-specific inputs was compared with those estimated using literature-based distributions. While modeled AERs were similar in magnitude to the measured AER they were consistently lower for all cities except Houston. AERs estimated using region-specific inputs were lower than those using study-specific inputs due to differences in window opening probabilities. The algorithm produced more spatially and temporally variable AERs compared with literature-based distributions reflecting within- and between-city differences, helping reduce error in estimates of air pollutant exposure. Published in the Journal of
Roatta, Silvestro; Arendt-Nielsen, Lars; Farina, Dario
2008-11-15
Animal and in vitro studies have shown that the sympathetic nervous system modulates the contractility of skeletal muscle fibres, which may require adjustments in the motor drive to the muscle in voluntary contractions. In this study, these mechanisms were investigated in the tibialis anterior muscle of humans during sympathetic activation induced by the cold pressor test (CPT; left hand immersed in water at 4 degrees C). In the first experiment, 11 healthy men performed 20 s isometric contractions at 10% of the maximal torque, before, during and after the CPT. In the second experiment, 12 healthy men activated a target motor unit at the minimum stable discharge rate for 5 min in the same conditions as in experiment 1. Intramuscular electromyographic (EMG) signals and torque were recorded and used to assess the motor unit discharge characteristics (experiment 1) and spike-triggered average twitch torque (experiment 2). CPT increased the diastolic blood pressure and heart rate by (mean +/- S.D.) 18 +/- 9 mmHg and 4.7 +/- 6.5 beats min(-1) (P < 0.01), respectively. In experiment 1, motor unit discharge rate increased from 10.4 +/- 1.0 pulses s(-1) before to 11.1 +/- 1.4 pulses s(-1) (P < 0.05) during the CPT. In experiment 2, the twitch half-relaxation time decreased by 15.8 +/- 9.3% (P < 0.05) during the CPT with respect to baseline. These results provide the first evidence of an adrenergic modulation of contractility of muscle fibres in individual motor units in humans, under physiological sympathetic activation.
Roatta, Silvestro; Arendt-Nielsen, Lars; Farina, Dario
2008-01-01
Animal and in vitro studies have shown that the sympathetic nervous system modulates the contractility of skeletal muscle fibres, which may require adjustments in the motor drive to the muscle in voluntary contractions. In this study, these mechanisms were investigated in the tibialis anterior muscle of humans during sympathetic activation induced by the cold pressor test (CPT; left hand immersed in water at 4°C). In the first experiment, 11 healthy men performed 20 s isometric contractions at 10% of the maximal torque, before, during and after the CPT. In the second experiment, 12 healthy men activated a target motor unit at the minimum stable discharge rate for 5 min in the same conditions as in experiment 1. Intramuscular electromyographic (EMG) signals and torque were recorded and used to assess the motor unit discharge characteristics (experiment 1) and spike-triggered average twitch torque (experiment 2). CPT increased the diastolic blood pressure and heart rate by (mean ±s.d.) 18 ± 9 mmHg and 4.7 ± 6.5 beats min−1 (P < 0.01), respectively. In experiment 1, motor unit discharge rate increased from 10.4 ± 1.0 pulses s−1 before to 11.1 ± 1.4 pulses s−1 (P < 0.05) during the CPT. In experiment 2, the twitch half-relaxation time decreased by 15.8 ± 9.3% (P < 0.05) during the CPT with respect to baseline. These results provide the first evidence of an adrenergic modulation of contractility of muscle fibres in individual motor units in humans, under physiological sympathetic activation. PMID:18818247
A supplementary approach for estimating reaeration rate coefficients
NASA Astrophysics Data System (ADS)
Jha, Ramakar; Ojha, C. S. P.; Bhatia, K. K. S.
2004-01-01
Different commonly used predictive equations for the reaeration rate coefficient (K2) have been evaluated using 231 data sets obtained from the literature and 576 data sets measured at different reaches of the River Kali in western Uttar Pradesh, India. The data sets include stream/channel velocity, bed slope, flow depth, cross-sectional area and reaeration rate coefficient (K2), obtained from the literature and generated during the field survey of River Kali, and were used to test the applicability of the predictive equations. The K2 values computed from the predictive equations have been compared with the corresponding K2 values measured in streams/channels. The performance of the predictive equations has been evaluated using different error estimation, namely standard error (SE), normal mean error (NME), mean multiplicative error (MME) and coefficient of determination (r2). The results show that the reaeration rate equation developed by Parkhurst and Pomeroy yielded the best agreement, with the values of SE, NME, MME and r2 as 33.387, 4.62, 3.58 and 0.95, respectively, for literature data sets (case 1) and 37.567, 3.57, 2.6 and 0.95, respectively, for all the data sets (literature data sets and River Kali data sets) (case 2). Further, to minimize error estimates and improve correlation between measured and computed reaeration rate coefficients, supplementary predictive equations have been developed based on Froude number criteria and a least-squares algorithm. The supplementary predictive equations have been verified using different error estimates and by comparing measured and computed reaeration rate coefficients for data sets not used in the development of the equations.
Phylogenetic estimates of diversification rate are affected by molecular rate variation.
Duchêne, D A; Hua, X; Bromham, L
2017-10-01
Molecular phylogenies are increasingly being used to investigate the patterns and mechanisms of macroevolution. In particular, node heights in a phylogeny can be used to detect changes in rates of diversification over time. Such analyses rest on the assumption that node heights in a phylogeny represent the timing of diversification events, which in turn rests on the assumption that evolutionary time can be accurately predicted from DNA sequence divergence. But there are many influences on the rate of molecular evolution, which might also influence node heights in molecular phylogenies, and thus affect estimates of diversification rate. In particular, a growing number of studies have revealed an association between the net diversification rate estimated from phylogenies and the rate of molecular evolution. Such an association might, by influencing the relative position of node heights, systematically bias estimates of diversification time. We simulated the evolution of DNA sequences under several scenarios where rates of diversification and molecular evolution vary through time, including models where diversification and molecular evolutionary rates are linked. We show that commonly used methods, including metric-based, likelihood and Bayesian approaches, can have a low power to identify changes in diversification rate when molecular substitution rates vary. Furthermore, the association between the rates of speciation and molecular evolution rate can cause the signature of a slowdown or speedup in speciation rates to be lost or misidentified. These results suggest that the multiple sources of variation in molecular evolutionary rates need to be considered when inferring macroevolutionary processes from phylogenies. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.
Estimation of multiple transmission rates for epidemics in heterogeneous populations.
Cook, Alex R; Otten, Wilfred; Marion, Glenn; Gibson, Gavin J; Gilligan, Christopher A
2007-12-18
One of the principal challenges in epidemiological modeling is to parameterize models with realistic estimates for transmission rates in order to analyze strategies for control and to predict disease outcomes. Using a combination of replicated experiments, Bayesian statistical inference, and stochastic modeling, we introduce and illustrate a strategy to estimate transmission parameters for the spread of infection through a two-phase mosaic, comprising favorable and unfavorable hosts. We focus on epidemics with local dispersal and formulate a spatially explicit, stochastic set of transition probabilities using a percolation paradigm for a susceptible-infected (S-I) epidemiological model. The S-I percolation model is further generalized to allow for multiple sources of infection including external inoculum and host-to-host infection. We fit the model using Bayesian inference and Markov chain Monte Carlo simulation to successive snapshots of damping-off disease spreading through replicated plant populations that differ in relative proportions of favorable and unfavorable hosts and with time-varying rates of transmission. Epidemiologically plausible parametric forms for these transmission rates are compared by using the deviance information criterion. Our results show that there are four transmission rates for a two-phase system, corresponding to each combination of infected donor and susceptible recipient. Knowing the number and magnitudes of the transmission rates allows the dominant pathways for transmission in a heterogeneous population to be identified. Finally, we show how failure to allow for multiple transmission rates can overestimate or underestimate the rate of spread of epidemics in heterogeneous environments, which could lead to marked failure or inefficiency of control strategies.
bz-rates: A Web Tool to Estimate Mutation Rates from Fluctuation Analysis.
Gillet-Markowska, Alexandre; Louvel, Guillaume; Fischer, Gilles
2015-09-02
Fluctuation analysis is the standard experimental method for measuring mutation rates in micro-organisms. The appearance of mutants is classically described by a Luria-Delbrück distribution composed of two parameters: the number of mutations per culture (m) and the differential growth rate between mutant and wild-type cells (b). A precise estimation of these two parameters is a prerequisite to the calculation of the mutation rate. Here, we developed bz-rates, a Web tool to calculate mutation rates that provides three useful advances over existing Web tools. First, it allows taking into account b, the differential growth rate between mutant and wild-type cells, in the estimation of m with the generating function. Second, bz-rates allows the user to take into account a deviation from the Luria-Delbrück distribution called z, the plating efficiency, in the estimation of m. Finally, the Web site provides a graphical visualization of the goodness-of-fit between the experimental data and the model. bz-rates is accessible at http://www.lcqb.upmc.fr/bzrates.
A note on evaporation from heated spikes
NASA Astrophysics Data System (ADS)
Urbassek, M.; Sigmund, P.
1984-09-01
We have investigated the effect of heat loss through evaporation on the surface temperature profile and the evaporation yield of an ion-induced spike. We derive a three-dimensional extension of a nonlinear integral equation first found by Mann and Wolf to describe the temperature profile in a semiinfinite medium in the presence of heat loss through the surface. The equation has been solved by perturbation expansion in powers of the evaporation rate. For heavy-ion induced, cylindrical elastic-collision spikes, noticeable but moderate corrections are found to evaporation yields estimated previously by neglecting heat loss due to evaporation. These results are relevant mainly to sputtering of metals by heavy atomic and molecular ion bombardment. Comments are also made on sputting of insulators both by heavy keV ions and by ionizing particles. Expressions for an effective sputter time and sputter area are derived for cylindrical geometry; both quantities turn out independent of the initial spike temperature. The sputter radius is normally greater than the depth of the crater formed; we conclude that the influence of crater formation on the evaporation yield is normally negligible.
Ancient hyaenas highlight the old problem of estimating evolutionary rates.
Shapiro, Beth; Ho, Simon Y W
2014-02-01
Phylogenetic analyses of ancient DNA data can provide a timeline for evolutionary change even in the absence of fossils. The power to infer the evolutionary rate is, however, highly dependent on the number and age of samples, the information content of the sequence data and the demographic history of the sampled population. In this issue of Molecular Ecology, Sheng et al. (2014) analysed mitochondrial DNA sequences isolated from a combination of ancient and present-day hyaenas, including three Pleistocene samples from China. Using an evolutionary rate inferred from the ages of the ancient sequences, they recalibrated the timing of hyaena diversification and suggest a much more recent evolutionary history than was believed previously. Their results highlight the importance of accurately estimating the evolutionary rate when inferring timescales of geographical and evolutionary diversification.
Banerjee, Arunava
2016-05-01
We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed that compares the spike train emitted by the output neuron of the network to the desired spike train by way of their putative impact on a virtual postsynaptic neuron. This formulation sidesteps the need for spike alignment and leads to closed-form solutions for all quantities of interest. Second, virtual assignment of weights to spikes rather than synapses enables a perturbation analysis of individual spike times and synaptic weights of the output, as well as all intermediate neurons in the network, which yields the gradients of the error functional with respect to the said entities. Learning proceeds via a gradient descent mechanism that leverages these quantities. Simulation experiments demonstrate the efficacy of the proposed learning framework. The experiments also highlight asymmetries between synapses on excitatory and inhibitory neurons.
Rotzoll, K.; El-Kadi, A. I.; Gingerich, S.B.
2007-01-01
In recent years the ground-water demand of the population of the island of Maui, Hawaii, has significantly increased. To ensure prudent management of the ground-water resources, an improved understanding of ground-water flow systems is needed. At present, large-scale estimations of aquifer properties are lacking for Maui. Seven analytical methods using constant-rate and variable-rate withdrawals for single wells provide an estimate of hydraulic conductivity and transmissivity for 103 wells in central Maui. Methods based on constant-rate tests, although not widely used on Maui, offer reasonable estimates. Step-drawdown tests, which are more abundantly used than other tests, provide similar estimates as constant-rate tests. A numerical model validates the suitability of analytical solutions for step-drawdown tests and additionally provides an estimate of storage parameters. The results show that hydraulic conductivity is log-normally distributed and that for dike-free volcanic rocks it ranges over several orders of magnitude from 1 to 2,500 m/d. The arithmetic mean, geometric mean, and median values of hydraulic conductivity are respectively 520, 280, and 370 m/d for basalt and 80, 50, and 30 m/d for sediment. A geostatistical approach using ordinary kriging yields a prediction of hydraulic conductivity on a larger scale. Overall, the results are in agreement with values published for other Hawaiian islands. ?? 2007 American Water Resources Association.
Redefinition and global estimation of basal ecosystem respiration rate
Yuan, W.; Luo, Y.; Li, X.; Liu, S.; Yu, G.; Zhou, T.; Bahn, M.; Black, A.; Desai, A.R.; Cescatti, A.; Marcolla, B.; Jacobs, C.; Chen, J.; Aurela, M.; Bernhofer, C.; Gielen, B.; Bohrer, G.; Cook, D.R.; Dragoni, D.; Dunn, A.L.; Gianelle, D.; Grnwald, T.; Ibrom, A.; Leclerc, M.Y.; Lindroth, A.; Liu, H.; Marchesini, L.B.; Montagnani, L.; Pita, G.; Rodeghiero, M.; Rodrigues, A.; Starr, G.; Stoy, P.C.
2011-01-01
Basal ecosystem respiration rate (BR), the ecosystem respiration rate at a given temperature, is a common and important parameter in empirical models for quantifying ecosystem respiration (ER) globally. Numerous studies have indicated that BR varies in space. However, many empirical ER models still use a global constant BR largely due to the lack of a functional description for BR. In this study, we redefined BR to be ecosystem respiration rate at the mean annual temperature. To test the validity of this concept, we conducted a synthesis analysis using 276 site-years of eddy covariance data, from 79 research sites located at latitudes ranging from ∼3°S to ∼70°N. Results showed that mean annual ER rate closely matches ER rate at mean annual temperature. Incorporation of site-specific BR into global ER model substantially improved simulated ER compared to an invariant BR at all sites. These results confirm that ER at the mean annual temperature can be considered as BR in empirical models. A strong correlation was found between the mean annual ER and mean annual gross primary production (GPP). Consequently, GPP, which is typically more accurately modeled, can be used to estimate BR. A light use efficiency GPP model (i.e., EC-LUE) was applied to estimate global GPP, BR and ER with input data from MERRA (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate resolution Imaging Spectroradiometer). The global ER was 103 Pg C yr −1, with the highest respiration rate over tropical forests and the lowest value in dry and high-latitude areas.
Likelihood of tree topologies with fossils and diversification rate estimation.
Didier, Gilles; Fau, Marine; Laurin, Michel
2017-04-18
Since the diversification process cannot be directly observed at the human scale, it has to be studied from the information available, namely the extant taxa and the fossil record. In this sense, phylogenetic trees including both extant taxa and fossils are the most complete representations of the diversification process that one can get. Such phylogenetic trees can be reconstructed from molecular and morphological data, to some extent. Among the temporal information of such phylogenetic trees, fossil ages are by far the most precisely known (divergence times are inferences calibrated mostly with fossils). We propose here a method to compute the likelihood of a phylogenetic tree with fossils in which the only considered time information is the fossil ages, and apply it to the estimation of the diversification rates from such data. Since it is required in our computation, we provide a method for determining the probability of a tree topology under the standard diversification model.Testing 21 our approach on simulated data shows that the maximum likelihood rate estimates from the phylogenetic tree topology and the fossil dates are almost as accurate as those obtained by taking into account all the data, including the divergence times. Moreover, they are substantially more accurate than the estimates obtained only from the exact divergence times (without taking into account the fossil record).We also provide an empirical example composed of 50 Permo-carboniferous eupelycosaur (early synapsid) taxa ranging in age from about 315 Ma (Late Carboniferous) to 270 Ma (shortly after the end of the Early Permian). Our analyses suggest a speciation (cladogenesis, or birth) rate of about 0.1 per lineage and per My, a marginally lower extinction rate, and a considerable hidden paleobiodiversity of early synapsids. © The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email
Can we estimate bacterial growth rates from ribosomal RNA content?
Kemp, P.F.
1995-12-31
Several studies have demonstrated a strong relationship between the quantity of RNA in bacterial cells and their growth rate under laboratory conditions. It may be possible to use this relationship to provide information on the activity of natural bacterial communities, and in particular on growth rate. However, if this approach is to provide reliably interpretable information, the relationship between RNA content and growth rate must be well-understood. In particular, a requisite of such applications is that the relationship must be universal among bacteria, or alternately that the relationship can be determined and measured for specific bacterial taxa. The RNA-growth rate relationship has not been used to evaluate bacterial growth in field studies, although RNA content has been measured in single cells and in bulk extracts of field samples taken from coastal environments. These measurements have been treated as probable indicators of bacterial activity, but have not yet been interpreted as estimators of growth rate. The primary obstacle to such interpretations is a lack of information on biological and environmental factors that affect the RNA-growth rate relationship. In this paper, the available data on the RNA-growth rate relationship in bacteria will be reviewed, including hypotheses regarding the regulation of RNA synthesis and degradation as a function of growth rate and environmental factors; i.e. the basic mechanisms for maintaining RNA content in proportion to growth rate. An assessment of the published laboratory and field data, the current status of this research area, and some of the remaining questions will be presented.
Fast maximum likelihood joint estimation of frequency and frequency rate
NASA Astrophysics Data System (ADS)
Abatzoglou, Theagenis J.
1986-11-01
A fast maximum likelihood algorithm is presented that jointly estimates the frequency and frequency rate of a sinusoid corrupted by additive Gaussian white noise. It consists of a coarse search and a fine search. First, the two-dimensional frequency-frequency rate plane is subdivided into parallelograms whose size depends on the region of convergence of Newton's method used in maximizing the log-likelihood function (LLF). The size of the parallelogram is explicitly computed and is optimal for the method used. The coarse search consists of maximizing the LLF over the vertices of the parallelograms. Then, starting at the vertex where the LLF attained its maximum, a two-dimensional Newton's method to find the absolute maximum of the LLF is implemented. This last step consists of the fine search. The rate of convergence of Newton's method is cubic, and is extremely fast. Furthermore, Newton's method will converge after two iterations when the starting point used in the method lies within 75 percent of the distances defined by the parallelogram of convergence whose center coincides with the true values of frequency and frequency rate. In this case, the rms errors for frequency and frequency rate are practically equal to the Cramer-Rao bound at all signal-to-noise ratio of equal to or greater than 15 dB. The frequency-frequency rate ambiguity function is shown to be even, and its periodicities are extracted.
Wavelet-based Poisson rate estimation using the Skellam distribution
NASA Astrophysics Data System (ADS)
Hirakawa, Keigo; Baqai, Farhan; Wolfe, Patrick J.
2009-02-01
Owing to the stochastic nature of discrete processes such as photon counts in imaging, real-world data measurements often exhibit heteroscedastic behavior. In particular, time series components and other measurements may frequently be assumed to be non-iid Poisson random variables, whose rate parameter is proportional to the underlying signal of interest-witness literature in digital communications, signal processing, astronomy, and magnetic resonance imaging applications. In this work, we show that certain wavelet and filterbank transform coefficients corresponding to vector-valued measurements of this type are distributed as sums and differences of independent Poisson counts, taking the so-called Skellam distribution. While exact estimates rarely admit analytical forms, we present Skellam mean estimators under both frequentist and Bayes models, as well as computationally efficient approximations and shrinkage rules, that may be interpreted as Poisson rate estimation method performed in certain wavelet/filterbank transform domains. This indicates a promising potential approach for denoising of Poisson counts in the above-mentioned applications.
Estimation of evapotranspiration rate in irrigated lands using stable isotopes
NASA Astrophysics Data System (ADS)
Umirzakov, Gulomjon; Windhorst, David; Forkutsa, Irina; Brauer, Lutz; Frede, Hans-Georg
2013-04-01
Agriculture in the Aral Sea basin is the main consumer of water resources and due to the current agricultural management practices inefficient water usage causes huge losses of freshwater resources. There is huge potential to save water resources in order to reach a more efficient water use in irrigated areas. Therefore, research is required to reveal the mechanisms of hydrological fluxes in irrigated areas. This paper focuses on estimation of evapotranspiration which is one of the crucial components in the water balance of irrigated lands. Our main objective is to estimate the rate of evapotranspiration on irrigated lands and partitioning of evaporation into transpiration using stable isotopes measurements. Experiments has done in 2 different soil types (sandy and sandy loam) irrigated areas in Ferghana Valley (Uzbekistan). Soil samples were collected during the vegetation period. The soil water from these samples was extracted via a cryogenic extraction method and analyzed for the isotopic ratio of the water isotopes (2H and 18O) based on a laser spectroscopy method (DLT 100, Los Gatos USA). Evapotranspiration rates were estimated with Isotope Mass Balance method. The results of evapotranspiration obtained using isotope mass balance method is compared with the results of Catchment Modeling Framework -1D model results which has done in the same area and the same time.
Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events
Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon
2016-01-01
Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate. PMID:28066225
Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events.
Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon
2016-01-01
Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate.
Computer-Vision-Guided Human Pulse Rate Estimation: A Review.
Sikdar, Arindam; Behera, Santosh Kumar; Dogra, Debi Prosad
2016-01-01
Human pulse rate (PR) can be estimated in several ways, including measurement instruments that directly count the PR through contact- and noncontact-based approaches. Over the last decade, computer-vision-assisted noncontact-based PR estimation has evolved significantly. Such techniques can be adopted for clinical purposes to mitigate some of the limitations of contact-based techniques. However, existing vision-guided noncontact-based techniques have not been benchmarked with respect to a challenging dataset. In view of this, we present a systematic review of such techniques implemented over a uniform computing platform. We have simultaneously recorded the PR and video of 14 volunteers. Five sets of data have been recorded for every volunteer using five different experimental conditions by varying the distance from the camera and illumination condition. Pros and cons of the existing noncontact image- and video-based PR techniques have been discussed with respect to our dataset. Experimental evaluation suggests that image- or video-based PR estimation can be highly effective for nonclinical purposes, and some of these approaches are very promising toward developing clinical applications. The present review is the first in this field of contactless vision-guided PR estimation research.
Estimation of Glomerular Filtration Rate in Cynomolgus Monkeys (Macaca fascicularis)
IWAMA, Ryosuke; SATO, Tsubasa; SAKURAI, Ken; TAKASUNA, Kiyoshi; ICHIJO, Toshihiro; FURUHAMA, Kazuhisa; SATOH, Hiroshi
2014-01-01
ABSTRACT To estimate the glomerular filtration rate (GFR) in cynomolgus monkeys (Macaca fascicularis), a three-blood-sample method using iodixanol was assessed in comparison with the conventional multisample strategy using inulin. Iodixanol and inulin were coadministered intravenously 40 mg I/kg and 50 mg/kg, respectively, to male monkeys, followed by blood collection 60, 90 and 120 min later. A close correlation (r=0.96) was noted between the GFR values estimated by both methods. In clinically healthy monkeys, the basal values were determined to be 3.06 ± 0.50 ml/min/kg. This is the first report, suggesting that serum clearance of iodixanol is a ready-to-use tool for a screening the GFR in monkeys, although it is necessary to perform a more longitudinal study using animals with reduced renal function. PMID:24998395
The effects of misclassification biases on veteran suicide rate estimates.
Huguet, Nathalie; Kaplan, Mark S; McFarland, Bentson H
2014-01-01
We assessed the impact that possible veteran suicide misclassification biases (i.e., inaccuracy in ascertainment of veteran status on the death certificate and misclassification of suicide as other manner of death) have on veteran suicide rate estimates. We obtained suicide mortality data from the 2003-2010 National Violent Death Reporting System and the 2003-2010 Department of Defense Casualty Analysis System. We derived population estimates from the 2003-2010 American Community Survey and 2003-2010 Department of Veterans Affairs data. We computed veteran and nonveteran suicide rates. The results showed that suicide rates were minimally affected by the adjustment for the misclassification of current military personnel suicides as veterans. Moreover, combining suicides and deaths by injury of undetermined intent did not alter the conclusions. The National Violent Death Reporting System is a valid surveillance system for veteran suicide. However, more than half of younger (< 25 years) male and female suicides, labeled as veterans, were likely to have been current military personnel at the time of their death and misclassified on the death certificate.
The Effects of Misclassification Biases on Veteran Suicide Rate Estimates
Kaplan, Mark S.; McFarland, Bentson H.
2014-01-01
Objectives. We assessed the impact that possible veteran suicide misclassification biases (i.e., inaccuracy in ascertainment of veteran status on the death certificate and misclassification of suicide as other manner of death) have on veteran suicide rate estimates. Methods. We obtained suicide mortality data from the 2003–2010 National Violent Death Reporting System and the 2003–2010 Department of Defense Casualty Analysis System. We derived population estimates from the 2003–2010 American Community Survey and 2003–2010 Department of Veterans Affairs data. We computed veteran and nonveteran suicide rates. Results. The results showed that suicide rates were minimally affected by the adjustment for the misclassification of current military personnel suicides as veterans. Moreover, combining suicides and deaths by injury of undetermined intent did not alter the conclusions. Conclusions. The National Violent Death Reporting System is a valid surveillance system for veteran suicide. However, more than half of younger (< 25 years) male and female suicides, labeled as veterans, were likely to have been current military personnel at the time of their death and misclassified on the death certificate. PMID:24228669
Independent component analysis in spiking neurons.
Savin, Cristina; Joshi, Prashant; Triesch, Jochen
2010-04-22
Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing dependent plasticity and synaptic scaling with an intrinsic plasticity rule that regulates neuronal excitability to maximize information transmission. We show that a stochastically spiking neuron learns one independent component for inputs encoded either as rates or using spike-spike correlations. Furthermore, different independent components can be recovered, when the activity of different neurons is decorrelated by adaptive lateral inhibition.
Band reporting rates of waterfowl: does individual heterogeneity bias estimated survival rates?
White, Gary C; Cordes, Line S; Arnold, Todd W
2013-01-01
In capture–recapture studies, the estimation accuracy of demographic parameters is essential to the efficacy of management of hunted animal populations. Dead recovery models based upon the reporting of rings or bands are often used for estimating survival of waterfowl and other harvested species. However, distance from the ringing site or condition of the bird may introduce substantial individual heterogeneity in the conditional band reporting rates (r), which could cause bias in estimated survival rates (S) or suggest nonexistent individual heterogeneity in S. To explore these hypotheses, we ran two sets of simulations (n = 1000) in MARK using Seber's dead recovery model, allowing time variation on both S and r. This included a series of heterogeneity models, allowing substantial variation on logit(r), and control models with no heterogeneity. We conducted simulations using two different values of S: S = 0.60, which would be typical of dabbling ducks such as mallards (Anas platyrhynchos), and S = 0.80, which would be more typical of sea ducks or geese. We chose a mean reporting rate on the logit scale of −1.9459 with SD = 1.5 for the heterogeneity models (producing a back-transformed mean of 0.196 with SD = 0.196, median = 0.125) and a constant reporting rate for the control models of 0.196. Within these sets of simulations, estimation models where σS = 0 and σS > 0 (σS is SD of individual survival rates on the logit scale) were incorporated to investigate whether real heterogeneity in r would induce apparent individual heterogeneity in S. Models where σS = 0 were selected approximately 91% of the time over models where σS > 0. Simulation results showed < 0.05% relative bias in estimating survival rates except for models estimating σS > 0 when true S = 0.8, where relative bias was a modest 0.5%. These results indicate that considerable variation in reporting rates does not cause major bias in estimated survival rates of waterfowl, further highlighting
Estimation of uncertainty for fatigue growth rate at cryogenic temperatures
NASA Astrophysics Data System (ADS)
Nyilas, Arman; Weiss, Klaus P.; Urbach, Elisabeth; Marcinek, Dawid J.
2014-01-01
Fatigue crack growth rate (FCGR) measurement data for high strength austenitic alloys at cryogenic environment suffer in general from a high degree of data scatter in particular at ΔK regime below 25 MPa√m. Using standard mathematical smoothing techniques forces ultimately a linear relationship at stage II regime (crack propagation rate versus ΔK) in a double log field called Paris law. However, the bandwidth of uncertainty relies somewhat arbitrary upon the researcher's interpretation. The present paper deals with the use of the uncertainty concept on FCGR data as given by GUM (Guidance of Uncertainty in Measurements), which since 1993 is a recommended procedure to avoid subjective estimation of error bands. Within this context, the lack of a true value addresses to evaluate the best estimate by a statistical method using the crack propagation law as a mathematical measurement model equation and identifying all input parameters. Each parameter necessary for the measurement technique was processed using the Gaussian distribution law by partial differentiation of the terms to estimate the sensitivity coefficients. The combined standard uncertainty determined for each term with its computed sensitivity coefficients finally resulted in measurement uncertainty of the FCGR test result. The described procedure of uncertainty has been applied within the framework of ITER on a recent FCGR measurement for high strength and high toughness Type 316LN material tested at 7 K using a standard ASTM proportional compact tension specimen. The determined values of Paris law constants such as C0 and the exponent m as best estimate along with the their uncertainty value may serve a realistic basis for the life expectancy of cyclic loaded members.
The spike timing dependence of plasticity
Feldman, Daniel E.
2012-01-01
In spike timing-dependent plasticity (STDP), the order and precise temporal interval between presynaptic and postsynaptic spikes determine the sign and magnitude of long-term potentiation (LTP) or depression (LTD). STDP is widely utilized in models of circuit-level plasticity, development, and learning. However, spike timing is just one of several factors (including firing rate, synaptic cooperativity, and depolarization) that govern plasticity induction, and its relative importance varies across synapses and activity regimes. This review summarizes the forms, cellular mechanisms, and prevalence of STDP, and evaluates the evidence that spike timing is an important determinant of plasticity in vivo. PMID:22920249
Estimating rock and slag wool fiber dissolution rate from composition.
Eastes, W; Potter, R M; Hadley, J G
2000-12-01
A method was tested for calculating the dissolution rate constant in the lung for a wide variety of synthetic vitreous silicate fibers from the oxide composition in weight percent. It is based upon expressing the logarithm of the dissolution rate as a linear function of the composition and using a different set of coefficients for different types of fibers. The method was applied to 29 fiber compositions including rock and slag fibers as well as refractory ceramic and special-purpose, thin E-glass fibers and borosilicate glass fibers for which in vivo measurements have been carried out. These fibers had dissolution rates that ranged over a factor of about 400, and the calculated dissolution rates agreed with the in vivo values typically within a factor of 4. The method presented here is similar to one developed previously for borosilicate glass fibers that was accurate to a factor of 1.25. The present coefficients work over a much broader range of composition than the borosilicate ones but with less accuracy. The dissolution rate constant of a fiber may be used to estimate whether disease would occur in animal inhalation or intraperitoneal injection studies of that fiber.
Efforts to estimate pesticide degradation rates in subsurface ...
When pesticides are used in real-world settings, the objective is to be effective in pest eradication at the site of application, but also it is desired that the pesticide have minimal persistence and mobility as it migrates away from the application site. At the site of application, sorption on soil and surface-soil degradation rates both factor into the pesticides' persistence. But once it migrates to the subsurface vadose zone and/or aquifers, subsurface degradation rate is a factor as well. Unfortunately, numerous soil properties that might affect pesticide degradation rate vary by orders of magnitude in the subsurface environment, both spatially and temporally, e.g., organic-carbon concentration, oxygen concentration, redox conditions, pH and soil mineralogy. Consequently, estimation of subsurface pesticide degradation rates and, in tum, pesticide persistence and mobility in the environment, has remained a challenge. To address this intransigent uncertainty, we surveyed peer-reviewed literature to identify > 100 data pairs in which investigators reported pesticide degradation rates in both surface and subsurface soils, using internally consistent experimental methods. These > 100 data pairs represented >30 separate pesticides. When the > 100 subsurface half-lives were plotted against surface half-lives, a limiting line could be defined for which all subsurface half-lives but three fe ll below the line. Of the three data points plotting above the limiting li
Using Same-Hospital Readmission Rates to Estimate All-Hospital Readmission Rates
Gonzalez, Andrew A.; Shih, Terry; Dimick, Justin B.; Ghaferi, Amir A.
2014-01-01
Background Since October of 2012, Medicare’s Hospital Readmissions Reduction Program has fined 2,200 hospitals a total of $500 billion. While the program penalizes readmission to any hospital, many institutions can only track readmissions to their own hospitals. We sought to determine the extent to which same-hospital readmission rates may be used to estimate all-hospital readmission rates following major surgery. Study Design We evaluated 3,940 hospitals treating 741,656 Medicare fee-for-service beneficiaries undergoing coronary artery bypass grafting, hip fracture repair, or colectomy between 2006 and 2008. We used hierarchical logistic regression to calculate risk- and reliability-adjusted rates of 30-day readmission to the same hospital and to any hospital. We next evaluated the correlation between same-hospital and all-hospital rates. To analyze the impact on hospital profiling, we compared rankings based on same-hospital rates to those based on all-hospital rates. Results The mean risk- and reliability-adjusted all-hospital readmission rate was 13.2% (SD 1.5%) and mean same-hospital readmission rate was 8.4% (SD 1.1%). Depending upon operation, between 57% (colectomy) and 63% (coronary artery bypass grafting) of hospitals were reclassified when profiling was based on same-hospital readmission rates instead of on all-hospital readmission rates. This was particularly pronounced in the middle three quintiles where 66–73% of hospitals were reclassified. Conclusions In evaluating hospital profiling under Medicare’s Hospital Readmissions Reduction Program, same-hospital rates provide unstable estimates of all-hospital readmission rates. To better anticipate penalties, hospitals require novel approaches for accurately tracking the totality of their post-operative readmissions. PMID:25159017
Inverse method for estimating respiration rates from decay time series
NASA Astrophysics Data System (ADS)
Forney, D. C.; Rothman, D. H.
2012-09-01
Long-term organic matter decomposition experiments typically measure the mass lost from decaying organic matter as a function of time. These experiments can provide information about the dynamics of carbon dioxide input to the atmosphere and controls on natural respiration processes. Decay slows down with time, suggesting that organic matter is composed of components (pools) with varied lability. Yet it is unclear how the appropriate rates, sizes, and number of pools vary with organic matter type, climate, and ecosystem. To better understand these relations, it is necessary to properly extract the decay rates from decomposition data. Here we present a regularized inverse method to identify an optimally-fitting distribution of decay rates associated with a decay time series. We motivate our study by first evaluating a standard, direct inversion of the data. The direct inversion identifies a discrete distribution of decay rates, where mass is concentrated in just a small number of discrete pools. It is consistent with identifying the best fitting "multi-pool" model, without prior assumption of the number of pools. However we find these multi-pool solutions are not robust to noise and are over-parametrized. We therefore introduce a method of regularized inversion, which identifies the solution which best fits the data but not the noise. This method shows that the data are described by a continuous distribution of rates, which we find is well approximated by a lognormal distribution, and consistent with the idea that decomposition results from a continuum of processes at different rates. The ubiquity of the lognormal distribution suggest that decay may be simply described by just two parameters: a mean and a variance of log rates. We conclude by describing a procedure that estimates these two lognormal parameters from decay data. Matlab codes for all numerical methods and procedures are provided.
Inverse method for estimating respiration rates from decay time series
NASA Astrophysics Data System (ADS)
Forney, D. C.; Rothman, D. H.
2012-03-01
Long-term organic matter decomposition experiments typically measure the mass lost from decaying organic matter as a function of time. These experiments can provide information about the dynamics of carbon dioxide input to the atmosphere and controls on natural respiration processes. Decay slows down with time, suggesting that organic matter is composed of components (pools) with varied lability. Yet it is unclear how the appropriate rates, sizes, and number of pools vary with organic matter type, climate, and ecosystem. To better understand these relations, it is necessary to properly extract the decay rates from decomposition data. Here we present a regularized inverse method to identify an optimally-fitting distribution of decay rates associated with a decay time series. We motivate our study by first evaluating a standard, direct inversion of the data. The direct inversion identifies a discrete distribution of decay rates, where mass is concentrated in just a small number of discrete pools. It is consistent with identifying the best fitting "multi-pool" model, without prior assumption of the number of pools. However we find these multi-pool solutions are not robust to noise and are over-parametrized. We therefore introduce a method of regularized inversion, which identifies the solution which best fits the data but not the noise. This method shows that the data are described by a continuous distribution of rates which we find is well approximated by a lognormal distribution, and consistent with the idea that decomposition results from a continuum of processes at different rates. The ubiquity of the lognormal distribution suggest that decay may be simply described by just two parameters; a mean and a variance of log rates. We conclude by describing a procedure that estimates these two lognormal parameters from decay data. Matlab codes for all numerical methods and procedures are provided.
Functional response models to estimate feeding rates of wading birds
Collazo, J.A.; Gilliam, J.F.; Miranda-Castro, L.
2010-01-01
Forager (predator) abundance may mediate feeding rates in wading birds. Yet, when modeled, feeding rates are typically derived from the purely prey-dependent Holling Type II (HoII) functional response model. Estimates of feeding rates are necessary to evaluate wading bird foraging strategies and their role in food webs; thus, models that incorporate predator dependence warrant consideration. Here, data collected in a mangrove swamp in Puerto Rico in 1994 were reanalyzed, reporting feeding rates for mixed-species flocks after comparing fits of the HoII model, as used in the original work, to the Beddington-DeAngelis (BD) and Crowley-Martin (CM) predator-dependent models. Model CM received most support (AIC c wi = 0.44), but models BD and HoII were plausible alternatives (AIC c ??? 2). Results suggested that feeding rates were constrained by predator abundance. Reductions in rates were attributed to interference, which was consistent with the independently observed increase in aggression as flock size increased (P < 0.05). Substantial discrepancies between the CM and HoII models were possible depending on flock sizes used to model feeding rates. However, inferences derived from the HoII model, as used in the original work, were sound. While Holling's Type II and other purely prey-dependent models have fostered advances in wading bird foraging ecology, evaluating models that incorporate predator dependence could lead to a more adequate description of data and processes of interest. The mechanistic bases used to derive models used here lead to biologically interpretable results and advance understanding of wading bird foraging ecology.
Simple estimates of vehicle-induced resuspension rates.
Escrig, A; Amato, F; Pandolfi, M; Monfort, E; Querol, X; Celades, I; Sanfélix, V; Alastuey, A; Orza, J A G
2011-10-01
Road dust emissions are considered to be a major source of airborne particulate matter (PM). This is particularly true for industrial environments, where there are high resuspension rates of deposited dust. The calculation of roads as PM emission sources has mostly focused on the consequences of this emission, viz. the increase in PM concentrations. That approach addresses the atmospheric transport of the emitted dust, and not its primary origin. In contrast, this paper examines the causes of the emission. The study is based on mass conservation of the dust deposited on the road surface. On the basis of this premise, estimates of emission rates were calculated from experimental data obtained in a road in a ceramic industrial area.
Empirical estimation of astrophysical photodisintegration rates of 106Cd
NASA Astrophysics Data System (ADS)
Belyshev, S. S.; Kuznetsov, A. A.; Stopani, K. A.
2017-09-01
It has been noted in previous experiments that the ratio between the photoneutron and photoproton disintegration channels of 106Cd might be considerably different from predictions of statistical models. The thresholds of these reactions differ by several MeV and the total astrophysical rate of photodisintegration of 106Cd, which is mostly produced in photonuclear reactions during the p-process nucleosynthesis, might be noticeably different from the calculated value. In this work the bremsstrahlung beam of a 55.6 MeV microtron and the photon activation technique is used to measure yields of photonuclear reaction products on isotopically-enriched cadmium targets. The obtained results are compared with predictions of statistical models. The experimental yields are used to estimate photodisintegration reaction rates on 106Cd, which are then used in nuclear network calculations to examine the effects of uncertainties on the produced abundences of p-nuclei.
Estimating the Rate of Occurrence of Renal Stones in Astronauts
NASA Technical Reports Server (NTRS)
Myers, J.; Goodenow, D.; Gokoglu, S.; Kassemi, M.
2016-01-01
Changes in urine chemistry, during and post flight, potentially increases the risk of renal stones in astronauts. Although much is known about the effects of space flight on urine chemistry, no inflight incidence of renal stones in US astronauts exists and the question "How much does this risk change with space flight?" remains difficult to accurately quantify. In this discussion, we tackle this question utilizing a combination of deterministic and probabilistic modeling that implements the physics behind free stone growth and agglomeration, speciation of urine chemistry and published observations of population renal stone incidences to estimate changes in the rate of renal stone presentation. The modeling process utilizes a Population Balance Equation based model developed in the companion IWS abstract by Kassemi et al. (2016) to evaluate the maximum growth and agglomeration potential from a specified set of urine chemistry values. Changes in renal stone occurrence rates are obtained from this model in a probabilistic simulation that interrogates the range of possible urine chemistries using Monte Carlo techniques. Subsequently, each randomly sampled urine chemistry undergoes speciation analysis using the well-established Joint Expert Speciation System (JESS) code to calculate critical values, such as ionic strength and relative supersaturation. The Kassemi model utilizes this information to predict the mean and maximum stone size. We close the assessment loop by using a transfer function that estimates the rate of stone formation from combining the relative supersaturation and both the mean and maximum free stone growth sizes. The transfer function is established by a simulation analysis which combines population stone formation rates and Poisson regression. Training this transfer function requires using the output of the aforementioned assessment steps with inputs from known non-stone-former and known stone-former urine chemistries. Established in a Monte Carlo
Hopenfeld, Bruce
2014-08-01
Most existing heart beat detection algorithms serially process peaks, which can be either noise or true beats. Serial processing can result in inaccurate detections in the context of high noise. The proposed method relies on the relative regularity of sinus rhythm RR interval changes to select the best sequences of peaks in a 5-10 s long segment of cardiac data. The best sequences with a current data segment are subjected to a trending analysis, to determine whether their associated RR intervals fit within a pattern of prior best segments. The RR regularity scores and the results of the trending analysis are combined into a single sequence score and the final sequence for a segment is chosen from the best sequences based on this overall score. The current heart rate estimate is updated with the final sequence's RR interval by an adaptive filter that weights the overall score. Twenty-four hour RR interval records for 54 normal individuals were parsed into 10-s segments and corrupted with spurious 'noise' peaks, which resulted in a revised RR interval series that included a number of false RR intervals. The algorithm was run on these corrupted RR interval series. The percentages of mean heart rate values within 5 beats min(-1) of the true value were 95%, 88% and 77% for 10, 20 and 30 added noise spikes, respectively. The percentages of mean heart rate values within 10 beats min(-1) of the true value were 98%, 96% and 91% for 10, 20 and 30 added noise spikes, respectively. Accuracy was higher for data segments characterized by relatively low RR interval variability. The proposed algorithm shows promise for estimating average heart rate for sinus rhythm in high noise environments.
Asymptotics of empirical eigenstructure for high dimensional spiked covariance
Wang, Weichen
2017-01-01
We derive the asymptotic distributions of the spiked eigenvalues and eigenvectors under a generalized and unified asymptotic regime, which takes into account the magnitude of spiked eigenvalues, sample size, and dimensionality. This regime allows high dimensionality and diverging eigenvalues and provides new insights into the roles that the leading eigenvalues, sample size, and dimensionality play in principal component analysis. Our results are a natural extension of those in Paul (2007) to a more general setting and solve the rates of convergence problems in Shen et al. (2013). They also reveal the biases of estimating leading eigenvalues and eigenvectors by using principal component analysis, and lead to a new covariance estimator for the approximate factor model, called shrinkage principal orthogonal complement thresholding (S-POET), that corrects the biases. Our results are successfully applied to outstanding problems in estimation of risks of large portfolios and false discovery proportions for dependent test statistics and are illustrated by simulation studies. PMID:28835726
Increasing fMRI sampling rate improves Granger causality estimates.
Lin, Fa-Hsuan; Ahveninen, Jyrki; Raij, Tommi; Witzel, Thomas; Chu, Ying-Hua; Jääskeläinen, Iiro P; Tsai, Kevin Wen-Kai; Kuo, Wen-Jui; Belliveau, John W
2014-01-01
Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.
Optimized support vector regression for drilling rate of penetration estimation
NASA Astrophysics Data System (ADS)
Bodaghi, Asadollah; Ansari, Hamid Reza; Gholami, Mahsa
2015-12-01
In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called `optimized support vector regression' is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.
Estimating glomerular filtration rate in a population-based study
Shankar, Anoop; Lee, Kristine E; Klein, Barbara EK; Muntner, Paul; Brazy, Peter C; Cruickshanks, Karen J; Nieto, F Javier; Danforth, Lorraine G; Schubert, Carla R; Tsai, Michael Y; Klein, Ronald
2010-01-01
Background: Glomerular filtration rate (GFR)-estimating equations are used to determine the prevalence of chronic kidney disease (CKD) in population-based studies. However, it has been suggested that since the commonly used GFR equations were originally developed from samples of patients with CKD, they underestimate GFR in healthy populations. Few studies have made side-by-side comparisons of the effect of various estimating equations on the prevalence estimates of CKD in a general population sample. Patients and methods: We examined a population-based sample comprising adults from Wisconsin (age, 43–86 years; 56% women). We compared the prevalence of CKD, defined as a GFR of <60 mL/min per 1.73 m2 estimated from serum creatinine, by applying various commonly used equations including the modification of diet in renal disease (MDRD) equation, Cockcroft–Gault (CG) equation, and the Mayo equation. We compared the performance of these equations against the CKD definition of cystatin C >1.23 mg/L. Results: We found that the prevalence of CKD varied widely among different GFR equations. Although the prevalence of CKD was 17.2% with the MDRD equation and 16.5% with the CG equation, it was only 4.8% with the Mayo equation. Only 24% of those identified to have GFR in the range of 50–59 mL/min per 1.73 m2 by the MDRD equation had cystatin C levels >1.23 mg/L; their mean cystatin C level was only 1 mg/L (interquartile range, 0.9–1.2 mg/L). This finding was similar for the CG equation. For the Mayo equation, 62.8% of those patients with GFR in the range of 50–59 mL/min per 1.73 m2 had cystatin C levels >1.23 mg/L; their mean cystatin C level was 1.3 mg/L (interquartile range, 1.2–1.5 mg/L). The MDRD and CG equations showed a false-positive rate of >10%. Discussion: We found that the MDRD and CG equations, the current standard to estimate GFR, appeared to overestimate the prevalence of CKD in a general population sample. PMID:20730018
Commercial Discount Rate Estimation for Efficiency Standards Analysis
Fujita, K. Sydny
2016-04-13
Underlying each of the Department of Energy's (DOE's) federal appliance and equipment standards are a set of complex analyses of the projected costs and benefits of regulation. Any new or amended standard must be designed to achieve significant additional energy conservation, provided that it is technologically feasible and economically justified (42 U.S.C. 6295(o)(2)(A)). A proposed standard is considered economically justified when its benefits exceed its burdens, as represented by the projected net present value of costs and benefits. DOE performs multiple analyses to evaluate the balance of costs and benefits of commercial appliance and equipment e efficiency standards, at the national and individual building or business level, each framed to capture different nuances of the complex impact of standards on the commercial end user population. The Life-Cycle Cost (LCC) analysis models the combined impact of appliance first cost and operating cost changes on a representative commercial building sample in order to identify the fraction of customers achieving LCC savings or incurring net cost at the considered efficiency levels.1 Thus, the choice of commercial discount rate value(s) used to calculate the present value of energy cost savings within the Life-Cycle Cost model implicitly plays a key role in estimating the economic impact of potential standard levels.2 This report is intended to provide a more in-depth discussion of the commercial discount rate estimation process than can be readily included in standard rulemaking Technical Support Documents (TSDs).
Diversity, Disparity, and Evolutionary Rate Estimation for Unresolved Yule Trees
Crawford, Forrest W.; Suchard, Marc A.
2013-01-01
The branching structure of biological evolution confers statistical dependencies on phenotypic trait values in related organisms. For this reason, comparative macroevolutionary studies usually begin with an inferred phylogeny that describes the evolutionary relationships of the organisms of interest. The probability of the observed trait data can be computed by assuming a model for trait evolution, such as Brownian motion, over the branches of this fixed tree. However, the phylogenetic tree itself contributes statistical uncertainty to estimates of rates of phenotypic evolution, and many comparative evolutionary biologists regard the tree as a nuisance parameter. In this article, we present a framework for analytically integrating over unknown phylogenetic trees in comparative evolutionary studies by assuming that the tree arises from a continuous-time Markov branching model called the Yule process. To do this, we derive a closed-form expression for the distribution of phylogenetic diversity (PD), which is the sum of branch lengths connecting the species in a clade. We then present a generalization of PD which is equivalent to the expected trait disparity in a set of taxa whose evolutionary relationships are generated by a Yule process and whose traits evolve by Brownian motion. We find expressions for the distribution of expected trait disparity under a Yule tree. Given one or more observations of trait disparity in a clade, we perform fast likelihood-based estimation of the Brownian variance for unresolved clades. Our method does not require simulation or a fixed phylogenetic tree. We conclude with a brief example illustrating Brownian rate estimation for 12 families in the mammalian order Carnivora, in which the phylogenetic tree for each family is unresolved. [Brownian motion; comparative method; Markov reward process; phylogenetic diversity; pure-birth process; quantitative trait evolution; trait disparity; Yule process.] PMID:23417679
Heart rate and estimated energy expenditure during ballroom dancing.
Blanksby, B A; Reidy, P W
1988-06-01
Ten competitive ballroom dance couples performed simulated competitive sequences of Modern and Latin American dance. Heart rate was telemetered during the dance sequences and related to direct measures of oxygen uptake and heart rate obtained while walking on a treadmill. Linear regression was employed to estimate gross and net energy expenditures of the dance sequences. A multivariate analysis of variance with repeated measures on the dance factor was applied to the data to test for interaction and main effects on the sex and dance factors. Overall mean heart rate values for the Modern dance sequence were 170 beats.min-1 and 173 beats.min-1 for males and females respectively. During the Latin American sequence mean overall heart rate for males was 168 beats.min-1 and 177 beats.min-1 for females. Predicted mean gross values of oxygen consumption for the males were 42.8 +/- 5.7 ml.kg-1 min-1 and 42.8 +/- 6.9 ml.kg-1 min-1 for the Modern and Latin American sequences respectively. Corresponding gross estimates of oxygen consumption for the females were 34.7 +/- 3.8 ml.kg-1 min-1 and 36.1 +/- 4.1 ml.kg-1 min-1. Males were estimated to expand 54.1 +/- 8.1 kJ.min-1 of energy during the Modern sequence and 54.0 +/- 9.6 kJ.min-1 during the Latin American sequence, while predicted energy expenditure for females was 34.7 +/- 3.8 kJ.min-1 and 36.1 +/- 4.1 kJ.min-1 for Modern and Latin American dance respectively. The results suggested that both males and females were dancing at greater than 80% of their maximum oxygen consumption. A significant difference between males and females was observed for predicted gross and net values of oxygen consumption (in L.min-1 and ml.kg-1 min-1).
Estimation of respiratory rate and heart rate during treadmill tests using acoustic sensor.
Popov, B; Sierra, G; Telfort, V; Agarwal, R; Lanzo, V
2005-01-01
The objective was to test the robustness of an acoustic method to estimate respiratory rates (RR) during treadmill test. The accuracy was assessed by the comparison with simultaneous estimates from a capnograph, using as a common reference a pneumotachometer. Eight subjects without any pulmonary disease were enrolled. Tracheal sounds were acquired using a contact piezoelectric sensor placed on the subject's throat and analyzed using a combined investigation of the sound envelope and frequency content. The capnograph and pneumotachometer were coupled to a face mask worn by the subjects. There was a strong linear correlation between all three methods (r^{2}ranged from 0.8 to 0.87), and the SEE ranged from 1.97 to 2.36. As a conclusion, the accuracy of the respiratory rate estimated from tracheal sounds on adult subjects during treadmill stress test was comparable to the accuracy of a commercial capnograph. The heart rate (HR) estimates can also be derived from carotid pulse using the same single sensor placed on the subject's throat. Compared to the pulse oximeter the results show an agreement of acoustic method with r^{2}=0.76 and SEE = 3.51.
Divergence of conserved non-coding sequences: rate estimates and relative rate tests.
Wagner, Günter P; Fried, Claudia; Prohaska, Sonja J; Stadler, Peter F
2004-11-01
In many eukaryotic genomes only a small fraction of the DNA codes for proteins, but the non-protein coding DNA harbors important genetic elements directing the development and the physiology of the organisms, like promoters, enhancers, insulators, and micro-RNA genes. The molecular evolution of these genetic elements is difficult to study because their functional significance is hard to deduce from sequence information alone. Here we propose an approach to the study of the rate of evolution of functional non-coding sequences at a macro-evolutionary scale. We identify functionally important non-coding sequences as Conserved Non-Coding Nucleotide (CNCN) sequences from the comparison of two outgroup species. The CNCN sequences so identified are then compared to their homologous sequences in a pair of ingroup species, and we monitor the degree of modification these sequences suffered in the two ingroup lineages. We propose a method to test for rate differences in the modification of CNCN sequences among the two ingroup lineages, as well as a method to estimate their rate of modification. We apply this method to the full sequences of the HoxA clusters from six gnathostome species: a shark, Heterodontus francisci; a basal ray finned fish, Polypterus senegalus; the amphibian, Xenopus tropicalis; as well as three mammalian species, human, rat and mouse. The results show that the evolutionary rate of CNCN sequences is not distinguishable among the three mammalian lineages, while the Xenopus lineage has a significantly increased rate of evolution. Furthermore the estimates of the rate parameters suggest that in the stem lineage of mammals the rate of CNCN sequence evolution was more than twice the rate observed within the placental amniotes clade, suggesting a high rate of evolution of cis-regulatory elements during the origin of amniotes and mammals. We conclude that the proposed methods can be used for testing hypotheses about the rate and pattern of evolution of putative
NASA Technical Reports Server (NTRS)
Oshman, Yaakov; Markley, Landis
1998-01-01
A sequential filtering algorithm is presented for attitude and attitude-rate estimation from Global Positioning System (GPS) differential carrier phase measurements. A third-order, minimal-parameter method for solving the attitude matrix kinematic equation is used to parameterize the filter's state, which renders the resulting estimator computationally efficient. Borrowing from tracking theory concepts, the angular acceleration is modeled as an exponentially autocorrelated stochastic process, thus avoiding the use of the uncertain spacecraft dynamic model. The new formulation facilitates the use of aiding vector observations in a unified filtering algorithm, which can enhance the method's robustness and accuracy. Numerical examples are used to demonstrate the performance of the method.
Drilling Penetration Rate Estimation using Rock Drillability Characterization Index
NASA Astrophysics Data System (ADS)
Taheri, Abbas; Qao, Qi; Chanda, Emmanuel
2016-10-01
Rock drilling Penetration Rate (PR) is influenced by many parameters including rock properties, machine parameters of the chosen rig and the working process. Five datasets were utilized to quantitatively assess the effect of various rock properties on PR. The datasets consisted of two sets of diamond and percussive drilling and one set of rotary drilling data. A new rating system called Rock Drillability Characterization index (RDCi) is proposed to predict PR for different drilling methods. This drillability model incorporates the uniaxial compressive strength of intact rock, the P-wave velocity and the density of rock. The RDCi system is further applied to predict PR in the diamond rotary drilling, non-coring rotary drilling and percussive drilling. Strong correlations between PR and RDCi values were observed indicating that the developed drillability rating model is relevant and can be utilized to effectively predict the rock drillability in any operating environment. A practical procedure for predicting PR using the RDCi was established. The drilling engineers can follow this procedure to use RDCi as an effective method to estimate drillability.
Estimating cougar predation rates from GPS location clusters
Anderson, C.R.; Lindzey, F.G.
2003-01-01
We examined cougar (Puma concolor) predation from Global Positioning System (GPS) location clusters (???2 locations within 200 m on the same or consecutive nights) of 11 cougars during September-May, 1999-2001. Location success of GPS averaged 2.4-5.0 of 6 location attempts/night/cougar. We surveyed potential predation sites during summer-fall 2000 and summer 2001 to identify prey composition (n = 74; 3-388 days post predation) and record predation-site variables (n = 97; 3-270 days post predation). We developed a model to estimate probability that a cougar killed a large mammal from data collected at GPS location clusters where the probability of predation increased with number of nights (defined as locations at 2200, 0200, or 0500 hr) of cougar presence within a 200-m radius (P < 0.001). Mean estimated cougar predation rates for large mammals were 7.3 days/kill for subadult females (1-2.5 yr; n = 3, 90% CI: 6.3 to 9.9), 7.0 days/kill for adult females (n = 2, 90% CI: 5.8 to 10.8), 5.4 days/kill for family groups (females with young; n = 3, 90% CI: 4.5 to 8.4), 9.5 days/kill for a subadult male (1-2.5 yr; n = 1, 90% CI: 6.9 to 16.4), and 7.8 days/kill for adult males (n = 2, 90% CI: 6.8 to 10.7). We may have slightly overestimated cougar predation rates due to our inability to separate scavenging from predation. We detected 45 deer (Odocoileus spp.), 15 elk (Cervus elaphus), 6 pronghorn (Antilocapra americana), 2 livestock, 1 moose (Alces alces), and 6 small mammals at cougar predation sites. Comparisons between cougar sexes suggested that females selected mule deer and males selected elk (P < 0.001). Cougars averaged 3.0 nights on pronghorn carcasses, 3.4 nights on deer carcasses, and 6.0 nights on elk carcasses. Most cougar predation (81.7%) occurred between 1901-0500 hr and peaked from 2201-0200 hr (31.7%). Applying GPS technology to identify predation rates and prey selection will allow managers to efficiently estimate the ability of an area's prey base to
Estimating rates of authigenic carbonate precipitation in modern marine sediments
NASA Astrophysics Data System (ADS)
Mitnick, E. H.; Lammers, L. N.; DePaolo, D. J.
2015-12-01
The formation of authigenic carbonate (AC) in marine sediments provides a plausible explanation for large, long-lasting marine δ13C excursions that does not require extreme swings in atmospheric O2 or CO2. AC precipitation during diagenesis is driven by alkalinity production during anaerobic organic matter oxidation and is coupled to sulfate reduction. To evaluate the extent to which this process contributes to global carbon cycling, we need to relate AC production to the geochemical and geomicrobiological processes and ocean chemical conditions that control it. We present a method to estimate modern rates of AC precipitation using an inversion approach based on the diffusion-advection-reaction equation and sediment pore fluid chemistry profiles as a function of depth. SEM images and semi-quantitative elemental map analyses provide further constraints. Our initial focus is on ODP sites 807 and 1082. We sum the diffusive, advective, and reactive terms that describe changes in pore fluid Ca and Mg concentrations due to precipitation of secondary carbonate. We calculate the advective and diffusive terms from the first and second derivatives of the Ca and Mg pore fluid concentrations using a spline fit to the data. Assuming steady-state behavior we derive net AC precipitation rates of up to 8 x 10-4 mmol m-2 y-1 for Site 807 and 0.6 mmol m-2 y-1 for Site 1082. Site 1082 sediments contain pyrite, which increases in amount down-section towards the estimated peak carbonate precipitation rate, consistent with sulfate-reduction-induced AC precipitation. However, the presence of gypsum and barite throughout the sediment column implies incomplete sulfate reduction and merits further investigation of the biogeochemical reactions controlling authigenesis. Further adjustments to our method could account for the small but non-negligible fraction of groundmass with a CaSO4 signature. Our estimates demonstrate that AC formation may represent a sizeable flux in the modern global
Chen, Zhe; Putrino, David F; Ghosh, Soumya; Barbieri, Riccardo; Brown, Emery N
2011-04-01
The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as effective methods for the identification of spiking dependency between neurons. However, unfavorable experimental conditions occasionally results in insufficient data collection due to factors such as low neuronal firing rates or brief recording periods, and in these cases, the standard maximum likelihood estimate becomes unreliable. The present studies compares the performance of different statistical inference procedures when applied to the estimation of functional connectivity in neuronal assemblies with sparse spiking data. Four inference methods were compared: maximum likelihood estimation, penalized maximum likelihood estimation, using either l(2) or l(1) regularization, and hierarchical Bayesian estimation based on a variational Bayes algorithm. Algorithmic performances were compared using well-established goodness-of-fit measures in benchmark simulation studies, and the hierarchical Bayesian approach performed favorably when compared with the other algorithms, and this approach was then successfully applied to real spiking data recorded from the cat motor cortex. The identification of spiking dependencies in physiologically acquired data was encouraging, since their sparse nature would have previously precluded them from successful analysis using traditional methods.
Wadehn, Federico; Carnal, David; Loeliger, Hans-Andrea
2015-08-01
Heart rate variability is one of the key parameters for assessing the health status of a subject's cardiovascular system. This paper presents a local model fitting algorithm used for finding single heart beats in photoplethysmogram recordings. The local fit of exponentially decaying cosines of frequencies within the physiological range is used to detect the presence of a heart beat. Using 42 subjects from the CapnoBase database, the average heart rate error was 0.16 BPM and the standard deviation of the absolute estimation error was 0.24 BPM.
Estimates of Lava Eruption Rates at Alba Patera, Mars
NASA Technical Reports Server (NTRS)
Baloga, S. M.; Pieri, D. C.
1985-01-01
The Martian volcanic complex Alba Patera exhibits a suite of well-defined, long and relatively narrow lava flows qualitatively resembling those found in Hawaii. Even without any information on the duration of the Martian flows, eruption rates (total volume discharge/duration of the extrusion) estimates are implied by the physical dimensions of the flows and the likely conjecture that Stephan-Boltzmann radiation is the dominating thermal loss mechanism. The ten flows in this analysis emanate radially from the central vent and were recently measured in length, plan areas, and average thicknesses by shadow measurement techniques. The dimensions of interest are shown. Although perhaps morphologically congruent to certain Hawaiian flows, the dramatically expanded physical dimensions of the Martian flows argues for some markedly distinct differences in lava flow composition for eruption characteristics.
Estimates of lava eruption rates at Alba Patera, Mars
NASA Astrophysics Data System (ADS)
Baloga, S. M.; Pieri, D. C.
1985-04-01
The Martian volcanic complex Alba Patera exhibits a suite of well-defined, long and relatively narrow lava flows qualitatively resembling those found in Hawaii. Even without any information on the duration of the Martian flows, eruption rates (total volume discharge/duration of the extrusion) estimates are implied by the physical dimensions of the flows and the likely conjecture that Stephan-Boltzmann radiation is the dominating thermal loss mechanism. The ten flows in this analysis emanate radially from the central vent and were recently measured in length, plan areas, and average thicknesses by shadow measurement techniques. The dimensions of interest are shown. Although perhaps morphologically congruent to certain Hawaiian flows, the dramatically expanded physical dimensions of the Martian flows argues for some markedly distinct differences in lava flow composition for eruption characteristics.
Groundwater recharge rate and zone structure estimation using PSOLVER algorithm.
Ayvaz, M Tamer; Elçi, Alper
2014-01-01
The quantification of groundwater recharge is an important but challenging task in groundwater flow modeling because recharge varies spatially and temporally. The goal of this study is to present an innovative methodology to estimate groundwater recharge rates and zone structures for regional groundwater flow models. Here, the unknown recharge field is partitioned into a number of zones using Voronoi Tessellation (VT). The identified zone structure with the recharge rates is associated through a simulation-optimization model that couples MODFLOW-2000 and the hybrid PSOLVER optimization algorithm. Applicability of this procedure is tested on a previously developed groundwater flow model of the Tahtalı Watershed. Successive zone structure solutions are obtained in an additive manner and penalty functions are used in the procedure to obtain realistic and plausible solutions. One of these functions constrains the optimization by forcing the sum of recharge rates for the grid cells that coincide with the Tahtalı Watershed area to be equal to the areal recharge rate determined in the previous modeling by a separate precipitation-runoff model. As a result, a six-zone structure is selected as the best zone structure that represents the areal recharge distribution. Comparison to results of a previous model for the same study area reveals that the proposed procedure significantly improves model performance with respect to calibration statistics. The proposed identification procedure can be thought of as an effective way to determine the recharge zone structure for groundwater flow models, in particular for situations where tangible information about groundwater recharge distribution does not exist.
Statistical Complexity of Neural Spike Trains
2014-08-28
SECURITY CLASSIFICATION OF: We present closed-form expressions for the entropy rate, statistical complexity, and predictive information for the spike...Triangle Park, NC 27709-2211 information, entropy rate, statistical complexity, excess entropy , integrate and fire neuron REPORT DOCUMENTATION PAGE 11...for the entropy rate, statistical complexity, and predictive information for the spike train of a single neuron in terms of the first passage time
Gambling disorder: estimated prevalence rates and risk factors in Macao.
Wu, Anise M S; Lai, Mark H C; Tong, Kwok-Kit
2014-12-01
An excessive, problematic gambling pattern has been regarded as a mental disorder in the Diagnostic and Statistical Manual for Mental Disorders (DSM) for more than 3 decades (American Psychiatric Association [APA], 1980). In this study, its latest prevalence in Macao (one of very few cities with legalized gambling in China and the Far East) was estimated with 2 major changes in the diagnostic criteria, suggested by the 5th edition of DSM (APA, 2013): (a) removing the "Illegal Act" criterion, and (b) lowering the threshold for diagnosis. A random, representative sample of 1,018 Macao residents was surveyed with a phone poll design in January 2013. After the 2 changes were adopted, the present study showed that the estimated prevalence rate of gambling disorder was 2.1% of the Macao adult population. Moreover, the present findings also provided empirical support to the application of these 2 recommended changes when assessing symptoms of gambling disorder among Chinese community adults. Personal risk factors of gambling disorder, namely being male, having low education, a preference for casino gambling, as well as high materialism, were identified.
NASA Astrophysics Data System (ADS)
Urdapilleta, Eugenio
2016-09-01
Spike generation in neurons produces a temporal point process, whose statistics is governed by intrinsic phenomena and the external incoming inputs to be coded. In particular, spike-evoked adaptation currents support a slow temporal process that conditions spiking probability at the present time according to past activity. In this work, we study the statistics of interspike interval correlations arising in such non-renewal spike trains, for a neuron model that reproduces different spike modes in a small adaptation scenario. We found that correlations are stronger as the neuron fires at a particular firing rate, which is defined by the adaptation process. When set in a subthreshold regime, the neuron may sustain this particular firing rate, and thus induce correlations, by noise. Given that, in this regime, interspike intervals are negatively correlated at any lag, this effect surprisingly implies a reduction in the variability of the spike count statistics at a finite noise intensity.
Improved Glomerular Filtration Rate Estimation by an Artificial Neural Network
Zhang, Yunong; Zhang, Xiang; Chen, Jinxia; Lv, Linsheng; Ma, Huijuan; Wu, Xiaoming; Zhao, Weihong; Lou, Tanqi
2013-01-01
Background Accurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance. Methods A total of 1,180 patients that had chronic kidney disease (CKD) were enrolled in the development data set, the internal validation data set and the external validation data set. Additional 222 patients that were admitted to two independent institutions were externally validated. Several ANNs were constructed and finally a Back Propagation network optimized by a genetic algorithm (GABP network) was chosen as a superior model, which included six input variables; i.e., serum creatinine, serum urea nitrogen, age, height, weight and gender, and estimated GFR as the one output variable. Performance was then compared with the Cockcroft-Gault equation, the MDRD equations and the CKD-EPI equation. Results In the external validation data set, Bland-Altman analysis demonstrated that the precision of the six-variable GABP network was the highest among all of the estimation models; i.e., 46.7 ml/min/1.73 m2 vs. a range from 71.3 to 101.7 ml/min/1.73 m2, allowing improvement in accuracy (15% accuracy, 49.0%; 30% accuracy, 75.1%; 50% accuracy, 90.5% [P<0.001 for all]) and CKD stage classification (misclassification rate of CKD stage, 32.4% vs. a range from 47.3% to 53.3% [P<0.001 for all]). Furthermore, in the additional external validation data set, precision and accuracy were improved by the six-variable GABP network. Conclusions A new ANN model (the six-variable GABP network) for CKD patients was developed that could provide a simple, more accurate and reliable means for the estimation of GFR and stage of CKD than traditional equations. Further validations are needed to assess the ability of the ANN model in diverse
Bayesian population decoding of spiking neurons.
Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias
2009-01-01
The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.
The time-rescaling theorem and its application to neural spike train data analysis.
Brown, Emery N; Barbieri, Riccardo; Ventura, Valérie; Kass, Robert E; Frank, Loren M
2002-02-01
Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model's validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point process neural spike train models, especially for histogram-based models such as perstimulus time histograms (PSTH) and rate functions estimated by spike train smoothing. The time-rescaling theorem is a well-known result in probability theory, which states that any point process with an integrable conditional intensity function may be transformed into a Poisson process with unit rate. We describe how the theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains. We apply these tests in two examples: a comparison of PSTH, inhomogeneous Poisson, and inhomogeneous Markov interval models of neural spike trains from the supplementary eye field of a macque monkey and a comparison of temporal and spatial smoothers, inhomogeneous Poisson, inhomogeneous gamma, and inhomogeneous inverse gaussian models of rat hippocampal place cell spiking activity. To help make the logic behind the time-rescaling theorem more accessible to researchers in neuroscience, we present a proof using only elementary probability theory arguments. We also show how the theorem may be used to simulate a general point process model of a spike train. Our paradigm makes it possible to compare parametric and histogram-based neural spike train models directly. These results suggest that the time-rescaling theorem can be a valuable tool for neural spike train data analysis.
The Second Spiking Threshold: Dynamics of Laminar Network Spiking in the Visual Cortex
Forsberg, Lars E.; Bonde, Lars H.; Harvey, Michael A.; Roland, Per E.
2016-01-01
Most neurons have a threshold separating the silent non-spiking state and the state of producing temporal sequences of spikes. But neurons in vivo also have a second threshold, found recently in granular layer neurons of the primary visual cortex, separating spontaneous ongoing spiking from visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary and moving visual stimuli from the spontaneous ongoing spiking state, in all layers and zones of areas 17 and 18 indicating that the second threshold is a property of the network. Spontaneous and evoked spiking, thus can easily be distinguished. In addition, the trajectories of spontaneous ongoing states were slow, frequently changing direction. In single trials, sharp as well as smooth and slow transients transform the trajectories to be outward directed, fast and crossing the threshold to become evoked. Although the speeds of the evolution of the evoked states differ, the same domain of the state space is explored indicating uniformity of the evoked states. All evoked states return to the spontaneous evoked spiking state as in a typical mono-stable dynamical system. In single trials, neither the original spiking rates, nor the temporal evolution in state space could distinguish simple visual scenes. PMID:27582693
Bernau, Christoph; Augustin, Thomas; Boulesteix, Anne-Laure
2013-09-01
High-dimensional binary classification tasks, for example, the classification of microarray samples into normal and cancer tissues, usually involve a tuning parameter. By reporting the performance of the best tuning parameter value only, over-optimistic prediction errors are obtained. For correcting this tuning bias, we develop a new method which is based on a decomposition of the unconditional error rate involving the tuning procedure, that is, we estimate the error rate of wrapper algorithms as introduced in the context of internal cross-validation (ICV) by Varma and Simon (2006, BMC Bioinformatics 7, 91). Our subsampling-based estimator can be written as a weighted mean of the errors obtained using the different tuning parameter values, and thus can be interpreted as a smooth version of ICV, which is the standard approach for avoiding tuning bias. In contrast to ICV, our method guarantees intuitive bounds for the corrected error. Additionally, we suggest to use bias correction methods also to address the conceptually similar method selection bias that results from the optimal choice of the classification method itself when evaluating several methods successively. We demonstrate the performance of our method on microarray and simulated data and compare it to ICV. This study suggests that our approach yields competitive estimates at a much lower computational price.
Capacity of a single spiking neuron
NASA Astrophysics Data System (ADS)
Ikeda, Shiro; Manton, Jonathan H.
2009-12-01
It is widely believed the neurons transmit information in the form of spikes. Since the spike patterns are known to be noisy, the neuron information channel is noisy. We have investigated the channel capacity of this "Spiking neuron channel" for both of the "temporal coding" and the "rate coding," which are two main coding considered in the neuroscience [1, 2]. As the result, we've proved that the distribution of inputs, which achieves the channel capacity, is a discrete distribution with finite mass points for temporal and rate coding under a reasonable assumption. In this draft, we show the details of the proof.
Dendritic spikes veto inhibition.
Stuart, Greg J
2012-09-06
How inhibition regulates dendritic excitability is critical to an understanding of the way neurons integrate the many thousands of synaptic inputs they receive. In this issue of Neuron, Müller et al. (2012) show that inhibition blocks the generation of weak dendritic spikes, leaving strong dendritic spikes intact. Copyright © 2012 Elsevier Inc. All rights reserved.
Neuronal communication: firing spikes with spikes.
Brecht, Michael
2012-08-21
Spikes of single cortical neurons can exert powerful effects even though most cortical synapses are too weak to fire postsynaptic neurons. A recent study combining single-cell stimulation with population imaging has visualized in vivo postsynaptic firing in genetically identified target cells. The results confirm predictions from in vitro work and might help to understand how the brain reads single-neuron activity.
Inference of neuronal network spike dynamics and topology from calcium imaging data.
Lütcke, Henry; Gerhard, Felipe; Zenke, Friedemann; Gerstner, Wulfram; Helmchen, Fritjof
2013-01-01
Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence ("spike trains") from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties.
Inference of neuronal network spike dynamics and topology from calcium imaging data
Lütcke, Henry; Gerhard, Felipe; Zenke, Friedemann; Gerstner, Wulfram; Helmchen, Fritjof
2013-01-01
Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence (“spike trains”) from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties. PMID:24399936
Program CONTRAST--A general program for the analysis of several survival or recovery rate estimates
Hines, J.E.; Sauer, J.R.
1989-01-01
This manual describes the use of program CONTRAST, which implements a generalized procedure for the comparison of several rate estimates. This method can be used to test both simple and composite hypotheses about rate estimates, and we discuss its application to multiple comparisons of survival rate estimates. Several examples of the use of program CONTRAST are presented. Program CONTRAST will run on IBM-cimpatible computers, and requires estimates of the rates to be tested, along with associated variance and covariance estimates.
NASA Astrophysics Data System (ADS)
Zea, Sven
1992-09-01
During a study of the spatial and temporal patterns of desmosponge (Porifera, Demospongiae) recruitment on rocky and coral reef habitats of Santa Marta, Colombian Caribbean Sea, preliminary attempts were made to estimate actual settlement rates from short-term (1 to a few days) recruitment censuses. Short-term recruitment rates on black, acrylic plastic plates attached to open, non-cryptic substratum by anchor screws were low and variable (0 5 recruits/plate in 1 2 days, sets of n=5 10 plates), but reflected the depth and seasonal trends found using mid-term (1 to a few months) censusing intervals. Moreover, mortality of recruits during 1 2 day intervals was low (0 12%). Thus, short-term censusing intervals can be used to estimate actual settlement rates. To be able to make statistical comparisons, however, it is necessary to increase the number of recruits per census by pooling data of n plates per set, and to have more than one set per site or treatment.
Estimating Examination Failure Rates and Reliability Prior to Administration.
ERIC Educational Resources Information Center
McIntosh, Vergil M.
Using estimates of item ease and item discrimination, procedures are provided for computing estimates of the reliability and percentage of failing scores for tests assembled from these items. Two assumptions are made: that the average item coefficient will be approximately equal to the average of the estimated coefficients and that the score…
From spiking neuron models to linear-nonlinear models.
Ostojic, Srdjan; Brunel, Nicolas
2011-01-20
Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.
Analyzing multiple spike trains with nonparametric Granger causality.
Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou
2009-08-01
Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.
Estimate or calculate? How surgeons rate volumes and surfaces.
Schuld, Jochen; Kollmar, Otto; Seidel, Roland; Black, Catherine; Schilling, Martin K; Richter, Sven
2012-06-01
Surgeons frequently describe the shape of intraoperative findings using visual judgement and their own sense of proportion or describing these findings in comparison to commonly used or metaphoric subjects. The aim of the study was to analyse the reliability of surgeon's estimations of dimensions. The study was performed in two phases. First, physicians had to estimate the metric proportions of four well-known objects. Second, surgeons were asked intraoperatively to estimate the liver resection surface after partial hepatectomy. The exact surface of the resection plane was measured using computed tomography-guided planimetry of the resection specimen. Physician's estimations and the exact measurements of the well-known objects and the liver resection surface were compared. Systematic error was defined by the natural logarithm of estimated/real size. We found a large individual discrepancy in estimating the metric proportions of commonly used objects and a tendency to underestimate both commonly used objects and liver resection surface. Experienced liver surgeons were more accurate in estimating liver resection surface compared with younger staff members. We found a large bias in estimating the dimension of both commonly used objects and the surface area of liver parenchyma transection. Obviously, estimating errors are more influenced by the individual subject who estimates than by the object itself. In clinical routine, surgeons should rely more on simple measuring devices than on their own sense of proportion. Education in how to estimate more correctly human liver resection surfaces can be achieved by ex vivo studies using porcine livers.
Prospective Coding by Spiking Neurons
Brea, Johanni; Gaál, Alexisz Tamás; Senn, Walter
2016-01-01
Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting. How predictions are realized on the neuronal level and what plasticity rule underlies their learning is not well understood. Here we propose a biologically plausible synaptic plasticity rule to learn predictions on a single neuron level on a timescale of seconds. The learning rule allows a spiking two-compartment neuron to match its current firing rate to its own expected future discounted firing rate. For instance, if an originally neutral event is repeatedly followed by an event that elevates the firing rate of a neuron, the originally neutral event will eventually also elevate the neuron’s firing rate. The plasticity rule is a form of spike timing dependent plasticity in which a presynaptic spike followed by a postsynaptic spike leads to potentiation. Even if the plasticity window has a width of 20 milliseconds, associations on the time scale of seconds can be learned. We illustrate prospective coding with three examples: learning to predict a time varying input, learning to predict the next stimulus in a delayed paired-associate task and learning with a recurrent network to reproduce a temporally compressed version of a sequence. We discuss the potential role of the learning mechanism in classical trace conditioning. In the special case that the signal to be predicted encodes reward, the neuron learns to predict the discounted future reward and learning is closely related to the temporal difference learning algorithm TD(λ). PMID:27341100
Logier, R; De Jonckheere, J; Dassonneville, A; Jeanne, M
2016-08-01
Heart Rate Variability (HRV) analysis can be of precious help in most of clinical situations because it is able to quantify the Autonomic Nervous System (ANS) activity. The HRV high frequency (HF) content, related to the parasympathetic tone, reflects the ANS response to an external stimulus responsible of pain, stress or various emotions. We have previously developed the Analgesia Nociception Index (ANI), based on HRV high frequency content estimation, which quantifies continuously the vagal tone in order to guide analgesic drug administration during general anesthesia. This technology has been largely validated during the peri-operative period. Currently, ANI is obtained from a specific algorithm analyzing a time series representing successive heart periods measured on the electrocardiographic (ECG) signal. In the perspective of widening the application fields of this technology, in particular for homecare monitoring, it has become necessary to simplify signal acquisition by using e.g. a pulse plethysmographic (PPG) sensor. Even if Pulse Rate Variability (PRV) analysis issued from PPG sensors has been shown to be unreliable and a bad predictor of HRV analysis results, we have compared PRV and HRV both estimated by ANI as well as HF and HF/(HF+LF) spectral analysis on both signals.
Modeled estimates of soil and dust ingestion rates for children.
Ozkaynak, Halûk; Xue, Jianping; Zartarian, Valerie G; Glen, Graham; Smith, Luther
2011-04-01
Daily soil/dust ingestion rates typically used in exposure and risk assessments are based on tracer element studies, which have a number of limitations and do not separate contributions from soil and dust. This article presents an alternate approach of modeling soil and dust ingestion via hand and object mouthing of children, using EPA's SHEDS model. Results for children 3 to <6 years old show that mean and 95th percentile total ingestion of soil and dust values are 68 and 224 mg/day, respectively; mean from soil ingestion, hand-to-mouth dust ingestion, and object-to-mouth dust ingestion are 41 mg/day, 20 mg/day, and 7 mg/day, respectively. In general, hand-to-mouth soil ingestion was the most important pathway, followed by hand-to-mouth dust ingestion, then object-to-mouth dust ingestion. The variability results are most sensitive to inputs on surface loadings, soil-skin adherence, hand mouthing frequency, and hand washing frequency. The predicted total soil and dust ingestion fits a lognormal distribution with geometric mean = 35.7 and geometric standard deviation = 3.3. There are two uncertainty distributions, one below the 20th percentile and the other above. Modeled uncertainties ranged within a factor of 3-30. Mean modeled estimates for soil and dust ingestion are consistent with past information but lower than the central values recommended in the 2008 EPA Child-Specific Exposure Factors Handbook. This new modeling approach, which predicts soil and dust ingestion by pathway, source type, population group, geographic location, and other factors, offers a better characterization of exposures relevant to health risk assessments as compared to using a single value. © 2010 Society for Risk Analysis.
Estimating mental fatigue based on electroencephalogram and heart rate variability
NASA Astrophysics Data System (ADS)
Zhang, Chong; Yu, Xiaolin
2010-01-01
The effects of long term mental arithmetic task on psychology are investigated by subjective self-reporting measures and action performance test. Based on electroencephalogram (EEG) and heart rate variability (HRV), the impacts of prolonged cognitive activity on central nervous system and autonomic nervous system are observed and analyzed. Wavelet packet parameters of EEG and power spectral indices of HRV are combined to estimate the change of mental fatigue. Then wavelet packet parameters of EEG which change significantly are extracted as the features of brain activity in different mental fatigue state, support vector machine (SVM) algorithm is applied to differentiate two mental fatigue states. The experimental results show that long term mental arithmetic task induces the mental fatigue. The wavelet packet parameters of EEG and power spectral indices of HRV are strongly correlated with mental fatigue. The predominant activity of autonomic nervous system of subjects turns to the sympathetic activity from parasympathetic activity after the task. Moreover, the slow waves of EEG increase, the fast waves of EEG and the degree of disorder of brain decrease compared with the pre-task. The SVM algorithm can effectively differentiate two mental fatigue states, which achieves the maximum classification accuracy (91%). The SVM algorithm could be a promising tool for the evaluation of mental fatigue. Fatigue, especially mental fatigue, is a common phenomenon in modern life, is a persistent occupational hazard for professional. Mental fatigue is usually accompanied with a sense of weariness, reduced alertness, and reduced mental performance, which would lead the accidents in life, decrease productivity in workplace and harm the health. Therefore, the evaluation of mental fatigue is important for the occupational risk protection, productivity, and occupational health.
A Priori Estimation of Rate Constants for Unimolecular Decomposition Reactions
1979-02-01
priori theoretical predictions for the decomposition rate of the formyl and the methoxy radicals have been made by application of the Rice-Ramsperger...Kassel-Marcus Theory. An ArrheniLus rate coefficient expression is derived for the formyl radical decomposition, and a modified Arrhenius type rate...9 IV. A PREDICTED RATE CONSTANT FOR FORMYL RADICAL DECOMPOSITION. .. .... .................... ........19 V. SUMMtARY
Vibration (?) spikes during natural rain events
NASA Technical Reports Server (NTRS)
Short, David A.
1994-01-01
Limited analysis of optical rain gauge (ORG) data from shipboard and ground based sensors has shown the existence of spikes, possibly attributable to sensor vibration, while rain is occurring. An extreme example of this behavior was noted aboard the PRC#5 on the evening of December 24, 1992 as the ship began repositioning during a rain event in the TOGA/COARE IFA. The spikes are readily evident in the one-second resolution data, but may be indistinguishable from natural rain rate fluctuations in subsampled or averaged data. Such spikes result in increased rainfall totals.
NASA Astrophysics Data System (ADS)
Melnik, V. N.; Shevchuk, N. V.; Konovalenko, A. A.; Rucker, H. O.; Dorovskyy, V. V.; Poedts, S.; Lecacheux, A.
2014-05-01
We analyze and discuss the properties of decameter spikes observed in July - August 2002 by the UTR-2 radio telescope. These bursts have a short duration (about one second) and occur in a narrow frequency bandwidth (50 - 70 kHz). They are chaotically located in the dynamic spectrum. Decameter spikes are weak bursts: their fluxes do not exceed 200 - 300 s.f.u. An interesting feature of these spikes is the observed linear increase of the frequency bandwidth with frequency. This dependence can be explained in the framework of the plasma mechanism that causes the radio emission, taking into account that Langmuir waves are generated by fast electrons within a narrow angle θ≈13∘ - 18∘ along the direction of the electron propagation. In the present article we consider the problem of the short lifetime of decameter spikes and discuss why electrons generate plasma waves in limited regions.
Rate control algorithm based on frame complexity estimation for MVC
NASA Astrophysics Data System (ADS)
Yan, Tao; An, Ping; Shen, Liquan; Zhang, Zhaoyang
2010-07-01
Rate control has not been well studied for multi-view video coding (MVC). In this paper, we propose an efficient rate control algorithm for MVC by improving the quadratic rate-distortion (R-D) model, which reasonably allocate bit-rate among views based on correlation analysis. The proposed algorithm consists of four levels for rate bits control more accurately, of which the frame layer allocates bits according to frame complexity and temporal activity. Extensive experiments show that the proposed algorithm can efficiently implement bit allocation and rate control according to coding parameters.
Macroscopic Description for Networks of Spiking Neurons
NASA Astrophysics Data System (ADS)
Montbrió, Ernest; Pazó, Diego; Roxin, Alex
2015-04-01
A major goal of neuroscience, statistical physics, and nonlinear dynamics is to understand how brain function arises from the collective dynamics of networks of spiking neurons. This challenge has been chiefly addressed through large-scale numerical simulations. Alternatively, researchers have formulated mean-field theories to gain insight into macroscopic states of large neuronal networks in terms of the collective firing activity of the neurons, or the firing rate. However, these theories have not succeeded in establishing an exact correspondence between the firing rate of the network and the underlying microscopic state of the spiking neurons. This has largely constrained the range of applicability of such macroscopic descriptions, particularly when trying to describe neuronal synchronization. Here, we provide the derivation of a set of exact macroscopic equations for a network of spiking neurons. Our results reveal that the spike generation mechanism of individual neurons introduces an effective coupling between two biophysically relevant macroscopic quantities, the firing rate and the mean membrane potential, which together govern the evolution of the neuronal network. The resulting equations exactly describe all possible macroscopic dynamical states of the network, including states of synchronous spiking activity. Finally, we show that the firing-rate description is related, via a conformal map, to a low-dimensional description in terms of the Kuramoto order parameter, called Ott-Antonsen theory. We anticipate that our results will be an important tool in investigating how large networks of spiking neurons self-organize in time to process and encode information in the brain.
Blood cadmium and estimated glomerular filtration rate in Korean adults.
Hwangbo, Young; Weaver, Virginia M; Tellez-Plaza, Maria; Guallar, Eliseo; Lee, Byung-Kook; Navas-Acien, Ana
2011-12-01
Cadmium is a nephrotoxicant at high exposure levels. Few studies have evaluated the role of cadmium in kidney function at low-exposure levels. We evaluated the association of blood cadmium with estimated glomerular filtration rate (eGFR) in the Korean adult population. We evaluated 1,909 adults ≥ 20 years of age who participated in the 2005 Korean National Health and Nutrition Examination Survey and had blood cadmium determinations. eGFR was calculated using the Modification of Diet in Renal Disease equation. Blood cadmium geometric means were 1.57 μg/L for men and 1.49 μg/L for women. The difference in eGFR levels that compared participants in the highest versus lowest cadmium tertiles, after multivariable adjustment, was -1.85 [95% confidence interval (CI): -3.55, -0.16] mL/min per 1.73 m2 in women and 0.67 (-1.16, 2.50) mL/min per 1.73 m2 in men. Among men, the association between blood cadmium and eGFR was modified by blood lead levels (p-value for interaction = 0.048). The fully adjusted differences in eGFR levels for a 2-fold increase in blood cadmium levels were -1.14 (-3.35, 1.07) and 1.84 (0.54, 3.14) mL/min per 1.73 m2 in men with blood lead levels below and above the median (2.75 μg/dL), respectively. Elevated blood cadmium levels were associated with lower eGFR in women, which supports the role of cadmium as a risk factor for chronic kidney disease. In men, there was no overall association, although elevated blood cadmium levels were associated with higher eGFR levels in men with high blood lead levels and nonstatistically associated with lower eGFR levels in men with low blood lead levels.
Blood Cadmium and Estimated Glomerular Filtration Rate in Korean Adults
Hwangbo, Young; Weaver, Virginia M.; Tellez-Plaza, Maria; Guallar, Eliseo; Lee, Byung-Kook
2011-01-01
Background: Cadmium is a nephrotoxicant at high exposure levels. Few studies have evaluated the role of cadmium in kidney function at low-exposure levels. Objective: We evaluated the association of blood cadmium with estimated glomerular filtration rate (eGFR) in the Korean adult population. Methods: We evaluated 1,909 adults ≥ 20 years of age who participated in the 2005 Korean National Health and Nutrition Examination Survey and had blood cadmium determinations. eGFR was calculated using the Modification of Diet in Renal Disease equation. Results: Blood cadmium geometric means were 1.57 μg/L for men and 1.49 μg/L for women. The difference in eGFR levels that compared participants in the highest versus lowest cadmium tertiles, after multivariable adjustment, was –1.85 [95% confidence interval (CI): –3.55, –0.16] mL/min per 1.73 m2 in women and 0.67 (–1.16, 2.50) mL/min per 1.73 m2 in men. Among men, the association between blood cadmium and eGFR was modified by blood lead levels (p-value for interaction = 0.048). The fully adjusted differences in eGFR levels for a 2-fold increase in blood cadmium levels were –1.14 (–3.35, 1.07) and 1.84 (0.54, 3.14) mL/min per 1.73 m2 in men with blood lead levels below and above the median (2.75 μg/dL), respectively. Conclusion: Elevated blood cadmium levels were associated with lower eGFR in women, which supports the role of cadmium as a risk factor for chronic kidney disease. In men, there was no overall association, although elevated blood cadmium levels were associated with higher eGFR levels in men with high blood lead levels and nonstatistically associated with lower eGFR levels in men with low blood lead levels. PMID:21835726
ESTIMATION OF PHOSPHATE ESTER HYDROLYSIS RATE CONSTANTS - ALKALINE HYDROLYSIS
SPARC (SPARC Performs Automated Reasoning in Chemistry) chemical reactivity models were extended to allow the calculation of alkaline hydrolysis rate constants of phosphate esters in water. The rate is calculated from the energy difference between the initial and transition state...
ESTIMATION OF PHOSPHATE ESTER HYDROLYSIS RATE CONSTANTS - ALKALINE HYDROLYSIS
SPARC (SPARC Performs Automated Reasoning in Chemistry) chemical reactivity models were extended to allow the calculation of alkaline hydrolysis rate constants of phosphate esters in water. The rate is calculated from the energy difference between the initial and transition state...
ESTIMATION OF PHOSPHATE ESTER HYDROLYSIS RATE CONSTANTS. I. ALKALINE HYDROLYSIS
SPARC (SPARC Performs Automated Reasoning in Chemistry) chemical reactivity models were extended to allow the calculation of alkaline hydrolysis rate constants of phosphate esters in water. The rate is calculated from the energy difference between the initial and transition state...
ESTIMATION OF PHOSPHATE ESTER HYDROLYSIS RATE CONSTANTS. I. ALKALINE HYDROLYSIS
SPARC (SPARC Performs Automated Reasoning in Chemistry) chemical reactivity models were extended to allow the calculation of alkaline hydrolysis rate constants of phosphate esters in water. The rate is calculated from the energy difference between the initial and transition state...
Measuring multiple spike train synchrony.
Kreuz, Thomas; Chicharro, Daniel; Andrzejak, Ralph G; Haas, Julie S; Abarbanel, Henry D I
2009-10-15
Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.
This work summarizes advancements made that allow for better estimation of resting metabolic rate (RMR) and subsequent estimation of ventilation rates (i.e., total ventilation (V_{E}) and alveolar ventilation (V_{A})) for individuals of both genders and all ages. ...
Motor control by precisely timed spike patterns.
Srivastava, Kyle H; Holmes, Caroline M; Vellema, Michiel; Pack, Andrea R; Elemans, Coen P H; Nemenman, Ilya; Sober, Samuel J
2017-01-31
A fundamental problem in neuroscience is understanding how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence that the nervous system uses millisecond-scale variations in the timing of spikes within multispike patterns to control a vertebrate behavior-namely, respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision.
Initial-rate based method for estimating the maximum heterotrophic growth rate parameter (μHmax).
Fall, C; Hooijmans, C M; Esparza-Soto, M; Olguin, M T; Bâ, K M
2012-07-01
Currently, the method most used for measuring the maximum specific growth rate (μ(Hmax)) of heterotrophic biomass is by respirometry, using growth batch tests performed at high food/microorganism ratio. No other technique has been suggested, although the former approach was criticized for providing kinetic constants that could be unrepresentative of the original biomass. An alternative method (seed-increments) is proposed, which relies on measuring the initial rates of respiration (r(O2)(_ini)) at different seeding levels, in a single batch, and in the presence of excess readily biodegradable substrate (S(S)). The ASM1-based underlying equations were developed, which showed that μ(Hmax) could be estimated through the slope of the linear function of r(O2)(_ini)·(V(WW)+v(ML)) vs v(ML) (volume of mixed liquor inoculum); V(WW) represent the wastewater volume added. The procedure was tested, being easy to apply; the postulated linearity was constantly observed and the method is claimed to measure the characteristics of the biomass of interest. Copyright © 2012 Elsevier Ltd. All rights reserved.
Shimazaki, Hideaki; Amari, Shun-ichi; Brown, Emery N.; Grün, Sonja
2012-01-01
Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods
Rating curve uncertainty: A comparison of estimation methods
Mason, Jr., Robert R.; Kiang, Julie E.; Cohn, Timothy A.; Constantinescu, George; Garcia, Marcelo H.; Hanes, Dan
2016-01-01
The USGS is engaged in both internal development and collaborative efforts to evaluate existing methods for characterizing the uncertainty of streamflow measurements (gaugings), stage-discharge relations (ratings), and, ultimately, the streamflow records derived from them. This paper provides a brief overview of two candidate methods that may be used to characterize the uncertainty of ratings, and illustrates the results of their application to the ratings of the two USGS streamgages.
Estimation of alga growth stage and lipid content growth rate
NASA Technical Reports Server (NTRS)
Embaye, Tsegereda N. (Inventor); Trent, Jonathan D. (Inventor)
2012-01-01
Method and system for estimating a growth stage of an alga in an ambient fluid. Measured light beam absorption or reflection values through or from the alga and through an ambient fluid, in each of two or more wavelength sub-ranges, are compared with reference light beam absorption values for corresponding wavelength sub-ranges for in each alga growth stage to determine (1) which alga growth stage, if any, is more likely and (2) whether estimated lipid content of the alga is increasing or has peaked. Alga growth is preferably terminated when lipid content has approximately reached a maximum value.
Simplification of an MCNP model designed for dose rate estimation
NASA Astrophysics Data System (ADS)
Laptev, Alexander; Perry, Robert
2017-09-01
A study was made to investigate the methods of building a simplified MCNP model for radiological dose estimation. The research was done using an example of a complicated glovebox with extra shielding. The paper presents several different calculations for neutron and photon dose evaluations where glovebox elements were consecutively excluded from the MCNP model. The analysis indicated that to obtain a fast and reasonable estimation of dose, the model should be realistic in details that are close to the tally. Other details may be omitted.
Estimation of Weapon Yield From Inversion of Dose Rate Contours
2009-03-01
Zucchini .................................................................................... 76 Operation PLUMBBOB—Priscilla...Appendix E: ESS FOM ....................................................................................................112 Appendix F: Zucchini FOM...Relationship of Dose Rate Contour Area, Weather Grid, and AOI ............... 57 23. Zucchini FDC, DNA-EX, and HPAC Dose Rate Contours at 28KT
Redefinition and global estimation of basal ecosystem respiration rate
Yuan, Wenping; Luo, Yiqi; Li, Xianglan; Liu, Shuguang; Yu, Guirui; Zhou, Tao; Bahn, Michael; Black, Andy; Desai, Ankur R.; Cescatti, Alessandro; Cook, David R.
2011-12-01
Basal ecosystem respiration rate (BR), the ecosystem respiration rate at a given temperature, is a common and important parameter in empirical models for quantifying ecosystem respiration (ER) globally. Numerous studies have indicated that BR varies in space. However, many empirical ER models still use a global constant BR largely due to the lack of a functional description for BR. In this study, we redefined BR to be ecosystem respiration rate at the mean annual temperature. To test the validity of this concept, we conducted a synthesis analysis using 276 site‐years of eddy covariance data, from 79 research sites located at latitudes ranging from ∼3°S to ∼70°N. Results showed that mean annual ER rate closely matches ER rate at mean annual temperature. Incorporation of site‐specific BR into global ER model substantially improved simulated ER compared to an invariant BR at all sites. These results confirm that ER at the mean annual
Improved estimates of environmental copper release rates from antifouling products.
Finnie, Alistair A
2006-01-01
The US Navy Dome method for measuring copper release rates from antifouling paint in-service on ships' hulls can be considered to be the most reliable indicator of environmental release rates. In this paper, the relationship between the apparent copper release rate and the environmental release rate is established for a number of antifouling coating types using data from a variety of available laboratory, field and calculation methods. Apart from a modified Dome method using panels, all laboratory, field and calculation methods significantly overestimate the environmental release rate of copper from antifouling coatings. The difference is greatest for self-polishing copolymer antifoulings (SPCs) and smallest for certain erodible/ablative antifoulings, where the ASTM/ISO standard and the CEPE calculation method are seen to typically overestimate environmental release rates by factors of about 10 and 4, respectively. Where ASTM/ISO or CEPE copper release rate data are used for environmental risk assessment or regulatory purposes, it is proposed that the release rate values should be divided by a correction factor to enable more reliable generic environmental risk assessments to be made. Using a conservative approach based on a realistic worst case and accounting for experimental uncertainty in the data that are currently available, proposed default correction factors for use with all paint types are 5.4 for the ASTM/ISO method and 2.9 for the CEPE calculation method. Further work is required to expand this data-set and refine the correction factors through correlation of laboratory measured and calculated copper release rates with the direct in situ environmental release rate for different antifouling paints under a range of environmental conditions.
Using genetic data to estimate diffusion rates in heterogeneous landscapes.
Roques, L; Walker, E; Franck, P; Soubeyrand, S; Klein, E K
2016-08-01
Having a precise knowledge of the dispersal ability of a population in a heterogeneous environment is of critical importance in agroecology and conservation biology as it can provide management tools to limit the effects of pests or to increase the survival of endangered species. In this paper, we propose a mechanistic-statistical method to estimate space-dependent diffusion parameters of spatially-explicit models based on stochastic differential equations, using genetic data. Dividing the total population into subpopulations corresponding to different habitat patches with known allele frequencies, the expected proportions of individuals from each subpopulation at each position is computed by solving a system of reaction-diffusion equations. Modelling the capture and genotyping of the individuals with a statistical approach, we derive a numerically tractable formula for the likelihood function associated with the diffusion parameters. In a simulated environment made of three types of regions, each associated with a different diffusion coefficient, we successfully estimate the diffusion parameters with a maximum-likelihood approach. Although higher genetic differentiation among subpopulations leads to more accurate estimations, once a certain level of differentiation has been reached, the finite size of the genotyped population becomes the limiting factor for accurate estimation.
Variability and coding efficiency of noisy neural spike encoders.
Steinmetz, P N; Manwani, A; Koch, C
2001-01-01
Encoding synaptic inputs as a train of action potentials is a fundamental function of nerve cells. Although spike trains recorded in vivo have been shown to be highly variable, it is unclear whether variability in spike timing represents faithful encoding of temporally varying synaptic inputs or noise inherent in the spike encoding mechanism. It has been reported that spike timing variability is more pronounced for constant, unvarying inputs than for inputs with rich temporal structure. This could have significant implications for the nature of neural coding, particularly if precise timing of spikes and temporal synchrony between neurons is used to represent information in the nervous system. To study the potential functional role of spike timing variability, we estimate the fraction of spike timing variability which conveys information about the input for two types of noisy spike encoders--an integrate and fire model with randomly chosen thresholds and a model of a patch of neuronal membrane containing stochastic Na(+) and K(+) channels obeying Hodgkin-Huxley kinetics. The quality of signal encoding is assessed by reconstructing the input stimuli from the output spike trains using optimal linear mean square estimation. A comparison of the estimation performance of noisy neuronal models of spike generation enables us to assess the impact of neuronal noise on the efficacy of neural coding. The results for both models suggest that spike timing variability reduces the ability of spike trains to encode rapid time-varying stimuli. Moreover, contrary to expectations based on earlier studies, we find that the noisy spike encoding models encode slowly varying stimuli more effectively than rapidly varying ones.
ESTIMATION OF CARBOXYLIC ACID ESTER HYDROLYSIS RATE CONSTANTS
SPARC chemical reactivity models were extended to calculate hydrolysis rate constants for carboxylic acid esters from molecular structure. The energy differences between the initial state and the transition state for a molecule of interest are factored into internal and external...
ESTIMATION OF CARBOXYLIC ACID ESTER HYDROLYSIS RATE CONSTANTS
SPARC chemical reactivity models were extended to calculate hydrolysis rate constants for carboxylic acid esters from molecular structure. The energy differences between the initial state and the transition state for a molecule of interest are factored into internal and external...
SEE Rate Estimation: Model Complexity and Data Requirements
NASA Technical Reports Server (NTRS)
Ladbury, Ray
2008-01-01
Statistical Methods outlined in [Ladbury, TNS20071 can be generalized for Monte Carlo Rate Calculation Methods Two Monte Carlo Approaches: a) Rate based on vendor-supplied (or reverse-engineered) model SEE testing and statistical analysis performed to validate model; b) Rate calculated based on model fit to SEE data Statistical analysis very similar to case for CREME96. Information Theory allows simultaneous consideration of multiple models with different complexities: a) Model with lowest AIC usually has greatest predictive power; b) Model averaging using AIC weights may give better performance if several models have similar good performance; and c) Rates can be bounded for a given confidence level over multiple models, as well as over the parameter space of a model.
An Overview of Bayesian Methods for Neural Spike Train Analysis
2013-01-01
Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed. PMID:24348527
Chen, Rongda; Wang, Ze
2013-01-01
Recovery rate is essential to the estimation of the portfolio’s loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody’s. However, it has a fatal defect that it can’t fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody’s new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management. PMID:23874558
Chen, Rongda; Wang, Ze
2013-01-01
Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody's. However, it has a fatal defect that it can't fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody's new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management.
Smooth Nonparametric Estimation of the Failure Rate Function and its First Two Derivatives
NASA Astrophysics Data System (ADS)
Koshkin, G. M.
2016-10-01
The class of nonparametric estimators of kernel type is considered for the unknown failure rate function and its derivatives. The convergence of the suggested estimations in distribution and in the mean square sense to the unknown failure rate function and its derivatives is proved. The interval estimator of the failure rate function is constructed. Advantages of the nonparametric estimators in comparison with the parametric algorithms are discussed. The suggested estimators of the failure rate function can be used to solve problems of exploitation reliability of complex physical, technical, and software systems under uncertainty conditions.
Capture-recapture analysis for estimating manatee reproductive rates
Kendall, W.L.; Langtimm, C.A.; Beck, C.A.; Runge, M.C.
2004-01-01
Modeling the life history of the endangered Florida manatee (Trichechus manatus latirostris) is an important step toward understanding its population dynamics and predicting its response to management actions. We developed a multi-state mark-resighting model for data collected under Pollock's robust design. This model estimates breeding probability conditional on a female's breeding state in the previous year; assumes sighting probability depends on breeding state; and corrects for misclassification of a cow with first-year calf, by estimating conditional sighting probability for the calf. The model is also appropriate for estimating survival and unconditional breeding probabilities when the study area is closed to temporary emigration across years. We applied this model to photo-identification data for the Northwest and Atlantic Coast populations of manatees, for years 1982?2000. With rare exceptions, manatees do not reproduce in two consecutive years. For those without a first-year calf in the previous year, the best-fitting model included constant probabilities of producing a calf for the Northwest (0.43, SE = 0.057) and Atlantic (0.38, SE = 0.045) populations. The approach we present to adjust for misclassification of breeding state could be applicable to a large number of marine mammal populations.
Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting
Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca
2016-01-01
In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision. PMID:27857680
Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.
Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca
2016-01-01
In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.
Doiron, Brent; Noonan, Liza; Lemon, Neal; Turner, Ray W
2003-01-01
The estimation and detection of stimuli by sensory neurons is affected by factors that govern a transition from tonic to burst mode and the frequency characteristics of burst output. Pyramidal cells in the electrosensory lobe of weakly electric fish generate spike bursts for the purpose of stimulus detection. Spike bursts are generated during repetitive discharge when a frequency-dependent broadening of dendritic spikes increases current flow from dendrite to soma to potentiate a somatic depolarizing afterpotential (DAP). The DAP eventually triggers a somatic spike doublet with an interspike interval that falls inside the dendritic refractory period, blocking spike backpropagiation and the DAP. Repetition of this process gives rise to a rhythmic dendritic spike failure, termed conditional backpropagation, that converts cell output from tonic to burst discharge. Through in vitro recordings and compartmental modeling we show that burst frequency is regulated by the rate of DAP potentiation during a burst, which determines the time required to discharge the spike doublet that blocks backpropagation. DAP potentiation is magnified through a positive feedback process when an increase in dendritic spike duration activates persistent sodium current (I(NaP)). I(NaP) further promotes a slow depolarization that induces a shift from tonic to burst discharge over time. The results are consistent with a dynamical systems analysis that shows that the threshold separating tonic and burst discharge can be represented as a saddle-node bifurcation. The interaction between dendritic K(+) current and I(NaP) provides a physiological explanation for a variable time scale of bursting dynamics characteristic of such a bifurcation.
Millisecond solar radio spikes observed at 1420 MHz
NASA Astrophysics Data System (ADS)
Dabrowski, B. P.; Kus, A. J.
We present results from observations of narrowband solar millisecond radio spikes at 1420 MHz. Observing data were collected between February 2000 and December 2001 with the 15-m radio telescope at the Centre for Astronomy Nicolaus Copernicus University in Torun, Poland, equipped with a radio spectrograph that covered the 1352-1490 MHz frequency band. The radio spectrograph has 3 MHz frequency resolution and 80 microsecond time resolution. We analyzed the individual radio spike duration, bandwidth and rate of frequency drift. A part of the observed spikes showed well-outlined subtle structures. On dynamic radio spectrograms of the investigated events we notice complex structures formed by numerous individual spikes known as chains of spikes and distinctly different structure of columns. Positions of active regions connected with radio spikes emission were investigated. It turns out that most of them are located near the center of the solar disk, suggesting strong beaming of the spikes emission.
Regulation of spike timing in visual cortical circuits
Tiesinga, Paul; Fellous, Jean-Marc; Sejnowski, Terrence J.
2010-01-01
A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role. PMID:18200026
Introduction to spiking neural networks: Information processing, learning and applications.
Ponulak, Filip; Kasinski, Andrzej
2011-01-01
The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.
Accuracy Rates of Ancestry Estimation by Forensic Anthropologists Using Identified Forensic Cases.
Thomas, Richard M; Parks, Connie L; Richard, Adam H
2017-01-30
A common task in forensic anthropology involves the estimation of the ancestry of a decedent by comparing their skeletal morphology and measurements to skeletons of individuals from known geographic groups. However, the accuracy rates of ancestry estimation methods in actual forensic casework have rarely been studied. This article uses 99 forensic cases with identified skeletal remains to develop accuracy rates for ancestry estimations conducted by forensic anthropologists. The overall rate of correct ancestry estimation from these cases is 90.9%, which is comparable to most research-derived rates and those reported by individual practitioners. Statistical tests showed no significant difference in accuracy rates depending on examiner education level or on the estimated or identified ancestry. More recent cases showed a significantly higher accuracy rate. The incorporation of metric analyses into the ancestry estimate in these cases led to a higher accuracy rate.
2013-07-01
Simultaneous Position, Velocity, Attitude, Angular Rates, and Surface Parameter Estimation Using Astrometric and Photometric Observations...estimation is extended to include the various surface parameters associated with the bidirectional reflectance distribution function (BRDF... parameters are estimated simultaneously Keywords—estimation; data fusion; BRDF I. INTRODUCTION Wetterer and Jah [1] first demonstrated how brightness
Estimation of error rates in classification of distorted imagery.
Lahart, M J
1984-04-01
This correspondence considers the problem of matching image data to a large library of objects when the image is distorted. Two types of distortions are considered: blur-type, in which a transfer function is applied to Fourier components of the image, and scale-type, in which each Fourier component is mapped into another. The objects of the library are assumed to be normally distributed in an appropriate feature space. Approximate expressions are developed for classification error rates as a function of noise. The error rates they predict are compared with those from classification of artificial data, generated by a Gaussian random number generator, and with error rates from classification of actual data. It is demonstrated that, for classification purposes, distortions can be characterized by a small number of parameters.
Estimation of the nucleation rate by differential scanning calorimetry
NASA Technical Reports Server (NTRS)
Kelton, Kenneth F.
1992-01-01
A realistic computer model is presented for calculating the time-dependent volume fraction transformed during the devitrification of glasses, assuming the classical theory of nucleation and continuous growth. Time- and cluster-dependent nucleation rates are calculated by modeling directly the evolving cluster distribution. Statistical overlap in the volume fraction transformed is taken into account using the standard Johnson-Mehl-Avrami formalism. Devitrification behavior under isothermal and nonisothermal conditions is described. The model is used to demonstrate that the recent suggestion by Ray and Day (1990) that nonisothermal DSC studies can be used to determine the temperature for the peak nucleation rate, is qualitatively correct for lithium disilicate, the glass investigated.
Estimation of the nucleation rate by differential scanning calorimetry
NASA Technical Reports Server (NTRS)
Kelton, Kenneth F.
1992-01-01
A realistic computer model is presented for calculating the time-dependent volume fraction transformed during the devitrification of glasses, assuming the classical theory of nucleation and continuous growth. Time- and cluster-dependent nucleation rates are calculated by modeling directly the evolving cluster distribution. Statistical overlap in the volume fraction transformed is taken into account using the standard Johnson-Mehl-Avrami formalism. Devitrification behavior under isothermal and nonisothermal conditions is described. The model is used to demonstrate that the recent suggestion by Ray and Day (1990) that nonisothermal DSC studies can be used to determine the temperature for the peak nucleation rate, is qualitatively correct for lithium disilicate, the glass investigated.
Estimation of Kramers-Moyal coefficients at low sampling rates
NASA Astrophysics Data System (ADS)
Honisch, Christoph; Friedrich, Rudolf
2011-06-01
An optimization procedure for the estimation of Kramers-Moyal coefficients from stationary, one-dimensional, Markovian time series data is presented. The method takes advantage of a recently reported approach that allows one to calculate exact finite sampling interval effects by solving the adjoint Fokker-Planck equation. Therefore, it is well suited for the analysis of sparsely sampled time series. The optimization can be performed either by making a parametric ansatz for drift and diffusion functions or parameter free. We demonstrate the power of the method in several numerical examples with synthetic time series.
Rating Curve Estimation from Local Levels and Upstream Discharges
NASA Astrophysics Data System (ADS)
Franchini, M.; Mascellani, G.
2003-04-01
Current technology allows for low cost and easy level measurements while the discharge measurements are still difficult and expensive. Thus, these are rarely performed and usually not in flood conditions because of lack of safety and difficulty in activating the measurement team in due time. As a consequence, long series of levels are frequently available without the corresponding discharge values. However, for the purpose of planning, management of water resources and real time flood forecasting, discharge is needed and it is therefore essential to convert local levels into discharge values by using the appropriate rating curve. Over this last decade, several methods have been proposed to relate local levels at a site of interest to data recorded at a river section located upstream where a rating curve is available. Some of these methods are based on a routing approach which uses the Muskingum model structure in different ways; others are based on the entropy concepts. Lately, fuzzy logic has been applied more and more frequently in the framework of hydraulic and hydrologic problems and this has prompted to the authors to use it for synthesising the rating curves. A comparison between all these strategies is performed, highlighting the difficulties and advantages of each of them, with reference to a long reach of the Po river in Italy, where several hydrometers and the relevant rating curves are available, thus allowing for both a parameterization and validation of the different strategies.
Estimated migration rates under scenarios of global climate change.
Jay R. Malcolm; Adam Markham; Ronald P. Neilson; Michael. Oaraci
2002-01-01
Greefihouse-induced warming and resulting shifts in climatic zones may exceed the migration capabilities of some species. We used fourteen combinations of General Circulation Models (GCMs) and Global Vegetation Models (GVMs) to investigate possible migration rates required under CO2 doubled climatic forcing.
Modeled Estimates of Soil and Dust Ingestion Rates for Children
Daily soil/dust ingestion rates typically used in exposure and risk assessments are based on tracer element studies, which have a number of limitations and do not separate contributions from soil and dust. This article presents an alternate approach of modeling soil and dust inge...
Estimating Degree Attainment Rates of Freshmen: A Campus Perspective
ERIC Educational Resources Information Center
Arredondo, Marisol; Knight, Saskia
2006-01-01
While Chapman University's six-year graduation rate has been increasing steadily in the last few years, the institution continues to seek better ways to retain its students. Accordingly, our attention has focused on trying to identify freshman students who are more likely to be retained and those who are likely to depart college prior to degree…
Modeled Estimates of Soil and Dust Ingestion Rates for Children
Daily soil/dust ingestion rates typically used in exposure and risk assessments are based on tracer element studies, which have a number of limitations and do not separate contributions from soil and dust. This article presents an alternate approach of modeling soil and dust inge...
Borque, Paloma; Luke, Edward; Kollias, Pavlos
2016-05-27
Coincident profiling observations from Doppler lidars and radars are used to estimate the turbulence energy dissipation rate (ε) using three different data sources: (i) Doppler radar velocity (DRV), (ii) Doppler lidar velocity (DLV), and (iii) Doppler radar spectrum width (DRW) measurements. Likewise, the agreement between the derived ε estimates is examined at the cloud base height of stratiform warm clouds. Collocated ε estimates based on power spectra analysis of DRV and DLV measurements show good agreement (correlation coefficient of 0.86 and 0.78 for both cases analyzed here) during both drizzling and nondrizzling conditions. This suggests that unified (below and above cloud base) time-height estimates of ε in cloud-topped boundary layer conditions can be produced. This also suggests that eddy dissipation rate can be estimated throughout the cloud layer without the constraint that clouds need to be nonprecipitating. Eddy dissipation rate estimates based on DRW measurements compare well with the estimates based on Doppler velocity but their performance deteriorates as precipitation size particles are introduced in the radar volume and broaden the DRW values. And, based on this finding, a methodology to estimate the Doppler spectra broadening due to the spread of the drop size distribution is presented. Furthermore, the uncertainties in ε introduced by signal-to-noise conditions, the estimation of the horizontal wind, the selection of the averaging time window, and the presence of precipitation are discussed in detail.
Borque, Paloma; Luke, Edward; Kollias, Pavlos
2016-05-27
Coincident profiling observations from Doppler lidars and radars are used to estimate the turbulence energy dissipation rate (ε) using three different data sources: (i) Doppler radar velocity (DRV), (ii) Doppler lidar velocity (DLV), and (iii) Doppler radar spectrum width (DRW) measurements. Likewise, the agreement between the derived ε estimates is examined at the cloud base height of stratiform warm clouds. Collocated ε estimates based on power spectra analysis of DRV and DLV measurements show good agreement (correlation coefficient of 0.86 and 0.78 for both cases analyzed here) during both drizzling and nondrizzling conditions. This suggests that unified (below and abovemore » cloud base) time-height estimates of ε in cloud-topped boundary layer conditions can be produced. This also suggests that eddy dissipation rate can be estimated throughout the cloud layer without the constraint that clouds need to be nonprecipitating. Eddy dissipation rate estimates based on DRW measurements compare well with the estimates based on Doppler velocity but their performance deteriorates as precipitation size particles are introduced in the radar volume and broaden the DRW values. And, based on this finding, a methodology to estimate the Doppler spectra broadening due to the spread of the drop size distribution is presented. Furthermore, the uncertainties in ε introduced by signal-to-noise conditions, the estimation of the horizontal wind, the selection of the averaging time window, and the presence of precipitation are discussed in detail.« less
Borque, Paloma; Luke, Edward; Kollias, Pavlos
2016-05-27
Coincident profiling observations from Doppler lidars and radars are used to estimate the turbulence energy dissipation rate (ε) using three different data sources: (i) Doppler radar velocity (DRV), (ii) Doppler lidar velocity (DLV), and (iii) Doppler radar spectrum width (DRW) measurements. Likewise, the agreement between the derived ε estimates is examined at the cloud base height of stratiform warm clouds. Collocated ε estimates based on power spectra analysis of DRV and DLV measurements show good agreement (correlation coefficient of 0.86 and 0.78 for both cases analyzed here) during both drizzling and nondrizzling conditions. This suggests that unified (below and above cloud base) time-height estimates of ε in cloud-topped boundary layer conditions can be produced. This also suggests that eddy dissipation rate can be estimated throughout the cloud layer without the constraint that clouds need to be nonprecipitating. Eddy dissipation rate estimates based on DRW measurements compare well with the estimates based on Doppler velocity but their performance deteriorates as precipitation size particles are introduced in the radar volume and broaden the DRW values. And, based on this finding, a methodology to estimate the Doppler spectra broadening due to the spread of the drop size distribution is presented. Furthermore, the uncertainties in ε introduced by signal-to-noise conditions, the estimation of the horizontal wind, the selection of the averaging time window, and the presence of precipitation are discussed in detail.
Rate of convergence of k-step Newton estimators to efficient likelihood estimators
Steve Verrill
2007-01-01
We make use of Cramer conditions together with the well-known local quadratic convergence of Newton?s method to establish the asymptotic closeness of k-step Newton estimators to efficient likelihood estimators. In Verrill and Johnson [2007. Confidence bounds and hypothesis tests for normal distribution coefficients of variation. USDA Forest Products Laboratory Research...
Occupational Injury Rate Estimates in Magnetic Fusion Experiments
cadwallader, lee
2006-11-01
In nuclear facilities, there are two primary aspects of occupational safety. The first aspect is radiological safety, which has rightly been treated in detail in nuclear facilities. Radiological exposure data have been collected from the existing tokamaks to serve as forecasts for ITER radiation safety. The second aspect of occupational safety, “traditional” industrial safety, must also be considered for a complete occupational safety program. Industrial safety data on occupational injury rates from the JET and TFTR tokamaks, three accelerators, and U.S. nuclear fission plants have been collected to set industrial safety goals for the ITER operations staff. The results of this occupational safety data collection and analysis activity are presented here. The data show that an annual lost workday case rate of 0.3 incidents per 100 workers is a conceivable goal for ITER operations.
Estimation of Eddy Dissipation Rates from Mesoscale Model Simulations
NASA Technical Reports Server (NTRS)
Ahmad, Nashat N.; Proctor, Fred H.
2012-01-01
The Eddy Dissipation Rate is an important metric for representing the intensity of atmospheric turbulence and is used as an input parameter for predicting the decay of aircraft wake vortices. In this study, the forecasts of eddy dissipation rates obtained from the current state-of-the-art mesoscale model are evaluated for terminal area applications. The Weather Research and Forecast mesoscale model is used to simulate the planetary boundary layer at high horizontal and vertical mesh resolutions. The Bougeault-Lacarrer and the Mellor-Yamada-Janji schemes implemented in the Weather Research and Forecast model are evaluated against data collected during the National Aeronautics and Space Administration s Memphis Wake Vortex Field Experiment. Comparisons with other observations are included as well.
Current methods for estimating the rate of photorespiration in leaves.
Busch, F A
2013-07-01
Photorespiration is a process that competes with photosynthesis, in which Rubisco oxygenates, instead of carboxylates, its substrate ribulose 1,5-bisphosphate. The photorespiratory metabolism associated with the recovery of 3-phosphoglycerate is energetically costly and results in the release of previously fixed CO2. The ability to quantify photorespiration is gaining importance as a tool to help improve plant productivity in order to meet the increasing global food demand. In recent years, substantial progress has been made in the methods used to measure photorespiration. Current techniques are able to measure multiple aspects of photorespiration at different points along the photorespiratory C2 cycle. Six different methods used to estimate photorespiration are reviewed, and their advantages and disadvantages discussed.
Comparison of methods for estimating motor unit firing rate time series from firing times.
Liu, Lukai; Bonato, Paolo; Clancy, Edward A
2016-12-01
The central nervous system regulates recruitment and firing of motor units to modulate muscle tension. Estimation of the firing rate time series is typically performed by decomposing the electromyogram (EMG) into its constituent firing times, then lowpass filtering a constituent train of impulses. Little research has examined the performance of different estimation methods, particularly in the inevitable presence of decomposition errors. The study of electrocardiogram (ECG) and electroneurogram (ENG) firing rate time series presents a similar problem, and has applied novel simulation models and firing rate estimators. Herein, we adapted an ENG/ECG simulation model to generate realistic EMG firing times derived from known rates, and assessed various firing rate time series estimation methods. ENG/ECG-inspired rate estimation worked exceptionally well when EMG decomposition errors were absent, but degraded unacceptably with decomposition error rates of ⩾1%. Typical EMG decomposition error rates-even after expert manual review-are 3-5%. At realistic decomposition error rates, more traditional EMG smoothing approaches performed best, when optimal smoothing window durations were selected. This optimal window was often longer than the 400ms duration that is commonly used in the literature. The optimal duration decreased as the modulation frequency of firing rate increased, average firing rate increased and decomposition errors decreased. Examples of these rate estimation methods on physiologic data are also provided, demonstrating their influence on measures computed from the firing rate estimate. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Estimating division and death rates from CFSE data
NASA Astrophysics Data System (ADS)
de Boer, Rob J.; Perelson, Alan S.
2005-12-01
The division tracking dye, carboxyfluorescin diacetate succinimidyl ester (CFSE) is currently the most informative labeling technique for characterizing the division history of cells in the immune system. Gett and Hodgkin (Nat. Immunol. 1 (2000) 239-244) have proposed to normalize CFSE data by the 2-fold expansion that is associated with each division, and have argued that the mean of the normalized data increases linearly with time, t, with a slope reflecting the division rate p. We develop a number of mathematical models for the clonal expansion of quiescent cells after stimulation and show, within the context of these models, under which conditions this approach is valid. We compare three means of the distribution of cells over the CFSE profile at time t: the mean, [mu](t), the mean of the normalized distribution, [mu]2(t), and the mean of the normalized distribution excluding nondivided cells, .In the simplest models, which deal with homogeneous populations of cells with constant division and death rates, the normalized frequency distribution of the cells over the respective division numbers is a Poisson distribution with mean [mu]2(t)=pt, where p is the division rate. The fact that in the data these distributions seem Gaussian is therefore insufficient to establish that the times at which cells are recruited into the first division have a Gaussian variation because the Poisson distribution approaches the Gaussian distribution for large pt. Excluding nondivided cells complicates the data analysis because , and only approaches a slope p after an initial transient.In models where the first division of the quiescent cells takes longer than later divisions, all three means have an initial transient before they approach an asymptotic regime, which is the expected [mu](t)=2pt and . Such a transient markedly complicates the data analysis. After the same initial transients, the normalized cell numbers tend to decrease at a rate e-dt, where d is the death rate
Training Deep Spiking Neural Networks Using Backpropagation
Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael
2016-01-01
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations. PMID:27877107
Motor control by precisely timed spike patterns
Srivastava, Kyle H.; Holmes, Caroline M.; Vellema, Michiel; Pack, Andrea R.; Elemans, Coen P. H.; Nemenman, Ilya; Sober, Samuel J.
2017-01-01
A fundamental problem in neuroscience is understanding how sequences of action potentials (“spikes”) encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence that the nervous system uses millisecond-scale variations in the timing of spikes within multispike patterns to control a vertebrate behavior—namely, respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision. PMID:28100491
Training Deep Spiking Neural Networks Using Backpropagation.
Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael
2016-01-01
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.
Kwon, H Y; Kim, K S
1982-07-01
The rate of natural increase in population between the census in 1975 and 1980 is calculated with total population by sex. An abridged life table, based on the Coale and Demeny life table model, is used. The number of deaths from this life table is calculated by using age specific death rate. According to this number, each crude death rate for both sexes is calculated. The crude birth rate calculation is the difference between the rate of natural increase in population and the crude death rate. Each computed rate is as follows: natural increase rate: 1.98% (male), 1.83% (female), 1.91% (total); crude death rate: .547% (male), .546% (female), .547% (total); crude birth rate: 2.535% (male), 2.340% (female), 2.448% (total). In evaluating the crude death rate and crude birth rate result, the crude death rate is lower than expected. Crude death rate from the whole country fertility survey taken in 1974 is 7/1000 people. According to the whole country fertility survey data taken in 1976, the infant mortality rate in 1974 and 1975 are at 26% and 27.5% respectively, which is considered low. This low death rate in recent times is due to the decrease in the infant mortality rate and the decrease in death of the aged population. Calculated crude birth rate is 25.6/1000 persons for males, and 24/1000 for females. After the whole country fertility survey conducted in 1976, the crude birth rate is estimated at 24/1000 persons and crude birth rate in 1980 was estimated at 23.4 persons. Results are in line with the calculations of the Third Social Economic Development 5-year plan which was drafted by working staff in the population sector including the population professionals in the Bureau of Statistics of the Economic Planning Board.
Casualty Estimation Sub-Study: Disease and Nonbattle Injury Rates
1981-08-01
visits (15.39/1000/day). Of these, 110 (10%) were defined as heat exhaustion and another 176 (16%) as heat related. Trauma accounted for 36 percent of...rates for dental and other specific conditions are shown in Table 7. The respective disease to trauma ratios were 1.5:1, 1:1.5, and 1:1 for Irwin 1...combat non-effectiveness is largely unknown. Quantitative information from historical data has been sufficient only in the area of maxilofacial injury
Estimation of Gene Insertion/Deletion Rates with Missing Data.
Dang, Utkarsh J; Devault, Alison M; Mortimer, Tatum D; Pepperell, Caitlin S; Poinar, Hendrik N; Golding, G Brian
2016-10-01
Lateral gene transfer is an important mechanism for evolution among bacteria. Here, genome-wide gene insertion and deletion rates are modeled in a maximum-likelihood framework with the additional flexibility of modeling potential missing data. The performance of the models is illustrated using simulations and a data set on gene family phyletic patterns from Gardnerella vaginalis that includes an ancient taxon. A novel application involving pseudogenization/genome reduction magnitudes is also illustrated, using gene family data from Mycobacterium spp. Finally, an R package called indelmiss is available from the Comprehensive R Archive Network at https://cran.r-project.org/package=indelmiss, with support documentation and examples.
Spike-Based Population Coding and Working Memory
Boerlin, Martin; Denève, Sophie
2011-01-01
Compelling behavioral evidence suggests that humans can make optimal decisions despite the uncertainty inherent in perceptual or motor tasks. A key question in neuroscience is how populations of spiking neurons can implement such probabilistic computations. In this article, we develop a comprehensive framework for optimal, spike-based sensory integration and working memory in a dynamic environment. We propose that probability distributions are inferred spike-per-spike in recurrently connected networks of integrate-and-fire neurons. As a result, these networks can combine sensory cues optimally, track the state of a time-varying stimulus and memorize accumulated evidence over periods much longer than the time constant of single neurons. Importantly, we propose that population responses and persistent working memory states represent entire probability distributions and not only single stimulus values. These memories are reflected by sustained, asynchronous patterns of activity which make relevant information available to downstream neurons within their short time window of integration. Model neurons act as predictive encoders, only firing spikes which account for new information that has not yet been signaled. Thus, spike times signal deterministically a prediction error, contrary to rate codes in which spike times are considered to be random samples of an underlying firing rate. As a consequence of this coding scheme, a multitude of spike patterns can reliably encode the same information. This results in weakly correlated, Poisson-like spike trains that are sensitive to initial conditions but robust to even high levels of external neural noise. This spike train variability reproduces the one observed in cortical sensory spike trains, but cannot be equated to noise. On the contrary, it is a consequence of optimal spike-based inference. In contrast, we show that rate-based models perform poorly when implemented with stochastically spiking neurons. PMID:21379319
A rapid method to estimate Westergren sedimentation rates
NASA Astrophysics Data System (ADS)
Alexy, Tamas; Pais, Eszter; Meiselman, Herbert J.
2009-09-01
The erythrocyte sedimentation rate (ESR) is a nonspecific but simple and inexpensive test that was introduced into medical practice in 1897. Although it is commonly utilized in the diagnosis and follow-up of various clinical conditions, ESR has several limitations including the required 60 min settling time for the test. Herein we introduce a novel use for a commercially available computerized tube viscometer that allows the accurate prediction of human Westergren ESR rates in as little as 4 min. Owing to an initial pressure gradient, blood moves between two vertical tubes through a horizontal small-bore tube and the top of the red blood cell (RBC) column in each vertical tube is monitored continuously with an accuracy of 0.083 mm. Using data from the final minute of a blood viscosity measurement, a sedimentation index (SI) was calculated and correlated with results from the conventional Westergren ESR test. To date, samples from 119 human subjects have been studied and our results indicate a strong correlation between SI and ESR values (R2=0.92). In addition, we found a close association between SI and RBC aggregation indices as determined by an automated RBC aggregometer (R2=0.71). Determining SI on human blood is rapid, requires no special training and has minimal biohazard risk, thus allowing physicians to rapidly screen for individuals with elevated ESR and to monitor therapeutic responses.
Infrared imaging based hyperventilation monitoring through respiration rate estimation
NASA Astrophysics Data System (ADS)
Basu, Anushree; Routray, Aurobinda; Mukherjee, Rashmi; Shit, Suprosanna
2016-07-01
A change in the skin temperature is used as an indicator of physical illness which can be detected through infrared thermography. Thermograms or thermal images can be used as an effective diagnostic tool for monitoring and diagnosis of various diseases. This paper describes an infrared thermography based approach for detecting hyperventilation caused due to stress and anxiety in human beings by computing their respiration rates. The work employs computer vision techniques for tracking the region of interest from thermal video to compute the breath rate. Experiments have been performed on 30 subjects. Corner feature extraction using Minimum Eigenvalue (Shi-Tomasi) algorithm and registration using Kanade Lucas-Tomasi algorithm has been used here. Thermal signature around the extracted region is detected and subsequently filtered through a band pass filter to compute the respiration profile of an individual. If the respiration profile shows unusual pattern and exceeds the threshold we conclude that the person is stressed and tending to hyperventilate. Results obtained are compared with standard contact based methods which have shown significant correlations. It is envisaged that the thermal image based approach not only will help in detecting hyperventilation but can assist in regular stress monitoring as it is non-invasive method.
Estimation of Measurement Characteristics of Ultrasound Fetal Heart Rate Monitor
NASA Astrophysics Data System (ADS)
Noguchi, Yasuaki; Mamune, Hideyuki; Sugimoto, Suguru; Yoshida, Atsushi; Sasa, Hidenori; Kobayashi, Hisaaki; Kobayashi, Mitsunao
1995-05-01
Ultrasound fetal heart rate monitoring is very useful to determine the status of the fetus because it is noninvasive. In order to ensure the accuracy of the fetal heart rate (FHR) obtained from the ultrasound Doppler data, we measure the fetal electrocardiogram (ECG) directly and obtain the Doppler data simultaneously. The FHR differences of the Doppler data from the direct ECG data are concentrated at 0 bpm (beats per minute), and are practically symmetrical. The distribution is found to be very close to the Student's t distribution by the test of goodness of fit with the chi-square test. The spectral density of the FHR differences shows the white noise spectrum without any dominant peaks. Furthermore, the f-n (n>1) fluctuation is observed both with the ultrasound Doppler FHR and with the direct ECG FHR. Thus, it is confirmed that the FHR observation and observation of the f-n (n>1) fluctuation using the ultrasound Doppler FHR are as useful as the direct ECG.
Ryskamp, Daniel A; Witkovsky, Paul; Barabas, Peter; Huang, Wei; Koehler, Christopher; Akimov, Nikolay P; Lee, Suk Hee; Chauhan, Shiwani; Xing, Wei; Rentería, René C; Liedtke, Wolfgang; Krizaj, David
2011-05-11
Sustained increase in intraocular pressure represents a major risk factor for eye disease, yet the cellular mechanisms of pressure transduction in the posterior eye are essentially unknown. Here we show that the mouse retina expresses mRNA and protein for the polymodal transient receptor potential vanilloid 4 (TRPV4) cation channel known to mediate osmotransduction and mechanotransduction. TRPV4 antibodies labeled perikarya, axons, and dendrites of retinal ganglion cells (RGCs) and intensely immunostained the optic nerve head. Müller glial cells, but not retinal astrocytes or microglia, also expressed TRPV4 immunoreactivity. The selective TRPV4 agonists 4α-PDD and GSK1016790A elevated [Ca2+]i in dissociated RGCs in a dose-dependent manner, whereas the TRPV1 agonist capsaicin had no effect on [Ca2+](RGC). Exposure to hypotonic stimulation evoked robust increases in [Ca2+](RGC). RGC responses to TRPV4-selective agonists and hypotonic stimulation were absent in Ca2+ -free saline and were antagonized by the nonselective TRP channel antagonists Ruthenium Red and gadolinium, but were unaffected by the TRPV1 antagonist capsazepine. TRPV4-selective agonists increased the spiking frequency recorded from intact retinas recorded with multielectrode arrays. Sustained exposure to TRPV4 agonists evoked dose-dependent apoptosis of RGCs. Our results demonstrate functional TRPV4 expression in RGCs and suggest that its activation mediates response to membrane stretch leading to elevated [Ca2+]i and augmented excitability. Excessive Ca2+ influx through TRPV4 predisposes RGCs to activation of Ca2+ -dependent proapoptotic signaling pathways, indicating that TRPV4 is a component of the response mechanism to pathological elevations of intraocular pressure.
Estimating mixing rates from seismic images of oceanic structure
NASA Astrophysics Data System (ADS)
Sheen, K. L.; White, N. J.; Hobbs, R. W.
2009-09-01
An improved understanding of the spatial distribution of diapycnal mixing in the oceans is the key to elucidating how meridional overturning circulation is closed. The challenge is to develop techniques which can be used to determine the variation of diapycnal mixing as a function of space and time throughout the oceanic volume. One promising approach exploits seismic reflection imaging of thermohaline structure. We have applied spectral analysis techniques to fine-structure undulations observed on a seismic transect close to the Subantarctic Front in the South Atlantic Ocean. 91 horizontal spectra were fitted using a linear combination of a Garrett-Munk tow spectrum for internal waves and a Batchelor model for turbulence. The fit between theory and observation is excellent and enables us to deduce the spatial variability and context of diapycnal mixing rates, which range from 10-5 to 10-3.5m2s-1.
Decision tree rating scales for workload estimation: Theme and variations
NASA Technical Reports Server (NTRS)
Wierwille, W. W.; Skipper, J. H.; Rieger, C. A.
1984-01-01
The Modified Cooper-Harper (MCH) scale which is a sensitive indicator of workload in several different types of aircrew tasks was examined. The study determined if variations of the scale might provide greater sensitivity and the reasons for the sensitivity of the scale. The MCH scale and five newly devised scales were examined in two different aircraft simulator experiments in which pilot loading was treated as an independent variable. It is indicated that while one of the new scales may be more sensitive in a given experiment, task dependency is a problem. The MCH scale exhibits consistent senstivity and remains the scale recommended for general use. The MCH scale results are consistent with earlier experiments. The rating scale experiments are reported and the questionnaire results which were directed to obtain a better understanding of the reasons for the relative sensitivity of the MCH scale and its variations are described.
Decision Tree Rating Scales for Workload Estimation: Theme and Variations
NASA Technical Reports Server (NTRS)
Wietwille, W. W.; Skipper, J. H.; Rieger, C. A.
1984-01-01
The modified Cooper-Harper (MCH) scale has been shown to be a sensitive indicator of workload in several different types of aircrew tasks. The MCH scale was examined to determine if certain variations of the scale might provide even greater sensitivity and to determine the reasons for the sensitivity of the scale. The MCH scale and five newly devised scales were studied in two different aircraft simulator experiments in which pilot loading was treated as an independent variable. Results indicate that while one of the new scales may be more sensitive in a given experiment, task dependency is a problem. The MCH scale exhibits consistent sensitivity and remains the scale recommended for general use. The results of the rating scale experiments are presented and the questionnaire results which were directed at obtaining a better understanding of the reasons for the relative sensitivity of the MCH scale and its variations are described.
Read Data Transfer Rate Estimation in Optical Phase Multilevel Recording
NASA Astrophysics Data System (ADS)
Kikukawa, Atsushi; Mikami, Hideharu; Ide, Tatsuro; Osawa, Kentaro; Watanabe, Koichi
2012-08-01
The feasibility of increasing the read data transfer rate (DTR) by introducing optical phase multilevel recording technology was investigated using computer simulations. The signals read back from phase marks suffer from strong intersymbol interference (ISI) when the phase marks are recorded with a linear symbol density comparable to that of current optical disc systems; thus, the partial response most-likely (PRML) method is essential. The increase in the decoder size is a serious problem when applying the PRML method to multilevel signal decoding; however, it was shown that this can be resolved by applying run-length limited (RLL) modulations. With these, it was shown that it is possible to decode 4-ary phase-modulated signals with satisfactory performance using PRML. Therefore, we conclude that it is possible to at least double the read DTR by introducing the optical phase multilevel recording technology.
Decision Tree Rating Scales for Workload Estimation: Theme and Variations
NASA Technical Reports Server (NTRS)
Wietwille, W. W.; Skipper, J. H.; Rieger, C. A.
1984-01-01
The modified Cooper-Harper (MCH) scale has been shown to be a sensitive indicator of workload in several different types of aircrew tasks. The MCH scale was examined to determine if certain variations of the scale might provide even greater sensitivity and to determine the reasons for the sensitivity of the scale. The MCH scale and five newly devised scales were studied in two different aircraft simulator experiments in which pilot loading was treated as an independent variable. Results indicate that while one of the new scales may be more sensitive in a given experiment, task dependency is a problem. The MCH scale exhibits consistent sensitivity and remains the scale recommended for general use. The results of the rating scale experiments are presented and the questionnaire results which were directed at obtaining a better understanding of the reasons for the relative sensitivity of the MCH scale and its variations are described.
Continuous functions determined by spike trains of a neuron subject to stimulation.
Awiszus, F
1988-01-01
Several ways of estimating a continuous function from the spike train output of a neuron subjected to repeated stimuli are compared: (i) the probability of firing function estimated by a PST-histogram (ii) the rate of discharge function estimated by a "frequencygram" (Bessou et al. 1968) and (iii) the interspike-interval function which is introduced in this paper. For a special class of neuronal responses, called deterministic, these functions may be expressed in terms of each other. It is shown that the current clamped Hodgkin-Huxley model of an action potential encoding membrane (Hodgkin and Huxley 1952) is able to generate such deterministic responses. As an experimental example, a deterministic response of a primary muscle spindle afferent is used to demonstrate the estimation of the functions. Interpretability and numerical estimatability of these spike train describing functions are discussed for deterministic neuronal responses.
Geomagnetic spikes on the core-mantle boundary
Davies, Christopher; Constable, Catherine
2017-01-01
Extreme variations of Earth's magnetic field occurred in the Levant region around 1000 BC, when the field intensity rapidly rose and fell by a factor of 2. No coherent link currently exists between this intensity spike and the global field produced by the core geodynamo. Here we show that the Levantine spike must span >60° longitude at Earth's surface if it originates from the core–mantle boundary (CMB). Several low intensity data are incompatible with this geometric bound, though age uncertainties suggest these data could have sampled the field before the spike emerged. Models that best satisfy energetic and geometric constraints produce CMB spikes 8–22° wide, peaking at O(100) mT. We suggest that the Levantine spike reflects an intense CMB flux patch that grew in place before migrating northwest, contributing to growth of the dipole field. Estimates of Ohmic heating suggest that diffusive processes likely govern the ultimate decay of geomagnetic spikes. PMID:28555646
Geomagnetic spikes on the core-mantle boundary
NASA Astrophysics Data System (ADS)
Davies, Christopher; Constable, Catherine
2017-05-01
Extreme variations of Earth's magnetic field occurred in the Levant region around 1000 BC, when the field intensity rapidly rose and fell by a factor of 2. No coherent link currently exists between this intensity spike and the global field produced by the core geodynamo. Here we show that the Levantine spike must span >60° longitude at Earth's surface if it originates from the core-mantle boundary (CMB). Several low intensity data are incompatible with this geometric bound, though age uncertainties suggest these data could have sampled the field before the spike emerged. Models that best satisfy energetic and geometric constraints produce CMB spikes 8-22° wide, peaking at O(100) mT. We suggest that the Levantine spike reflects an intense CMB flux patch that grew in place before migrating northwest, contributing to growth of the dipole field. Estimates of Ohmic heating suggest that diffusive processes likely govern the ultimate decay of geomagnetic spikes.
NASA Astrophysics Data System (ADS)
Muchlisoh, Siti; Kurnia, Anang; Notodiputro, Khairil Anwar; Mangku, I. Wayan
2016-02-01
Labor force surveys conducted over time by the rotating panel design have been carried out in many countries, including Indonesia. Labor force survey in Indonesia is regularly conducted by Statistics Indonesia (Badan Pusat Statistik-BPS) and has been known as the National Labor Force Survey (Sakernas). The main purpose of Sakernas is to obtain information about unemployment rates and its changes over time. Sakernas is a quarterly survey. The quarterly survey is designed only for estimating the parameters at the provincial level. The quarterly unemployment rate published by BPS (official statistics) is calculated based on only cross-sectional methods, despite the fact that the data is collected under rotating panel design. The study purpose to estimate a quarterly unemployment rate at the district level used small area estimation (SAE) model by combining time series and cross-sectional data. The study focused on the application and comparison between the Rao-Yu model and dynamic model in context estimating the unemployment rate based on a rotating panel survey. The goodness of fit of both models was almost similar. Both models produced an almost similar estimation and better than direct estimation, but the dynamic model was more capable than the Rao-Yu model to capture a heterogeneity across area, although it was reduced over time.
Temporal Correlations and Neural Spike Train Entropy
Schultz, Simon R.; Panzeri, Stefano
2001-06-18
Sampling considerations limit the experimental conditions under which information theoretic analyses of neurophysiological data yield reliable results. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for limited samples of data. This approach also yields insight to the role of correlations between spikes in temporal coding mechanisms. The method, when applied to recordings from complex cells of the monkey primary visual cortex, results in lower rms error information estimates in comparison to a {open_quotes}brute force{close_quotes} approach.
Neuronal spike trains and stochastic point processes. I. The single spike train.
Perkel, D H; Gerstein, G L; Moore, G P
1967-07-01
In a growing class of neurophysiological experiments, the train of impulses ("spikes") produced by a nerve cell is subjected to statistical treatment involving the time intervals between spikes. The statistical techniques available for the analysis of single spike trains are described and related to the underlying mathematical theory, that of stochastic point processes, i.e., of stochastic processes whose realizations may be described as series of point events occurring in time, separated by random intervals. For single stationary spike trains, several orders of complexity of statistical treatment are described; the major distinction is that between statistical measures that depend in an essential way on the serial order of interspike intervals and those that are order-independent. The interrelations among the several types of calculations are shown, and an attempt is made to ameliorate the current nomenclatural confusion in this field. Applications, interpretations, and potential difficulties of the statistical techniques are discussed, with special reference to types of spike trains encountered experimentally. Next, the related types of analysis are described for experiments which involve repeated presentations of a brief, isolated stimulus. Finally, the effects of nonstationarity, e.g. long-term changes in firing rate, on the various statistical measures are discussed. Several commonly observed patterns of spike activity are shown to be differentially sensitive to such changes. A companion paper covers the analysis of simultaneously observed spike trains.
Two Approaches to Estimation of Classification Accuracy Rate under Item Response Theory
ERIC Educational Resources Information Center
Lathrop, Quinn N.; Cheng, Ying
2013-01-01
Within the framework of item response theory (IRT), there are two recent lines of work on the estimation of classification accuracy (CA) rate. One approach estimates CA when decisions are made based on total sum scores, the other based on latent trait estimates. The former is referred to as the Lee approach, and the latter, the Rudner approach,…
Estimating spread rates of non-native species: the gypsy moth as a case study
Patrick Tobin; Andrew M. Liebhold; E. Anderson Roberts; Laura M. Blackburn
2015-01-01
Estimating rates of spread and generating projections of future range expansion for invasive alien species is a key process in the development of management guidelines and policy. Critical needs to estimate spread rates include the availability of surveys to characterize the spatial distribution of an invading species and the application of analytical methods to...
Generalized activity equations for spiking neural network dynamics
Buice, Michael A.; Chow, Carson C.
2013-01-01
Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales—the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances. PMID:24298252
Smith, N G; Hurst, L D
1999-01-01
Miyata et al. have suggested that the male-to-female mutation rate ratio (alpha) can be estimated by comparing the neutral substitution rates of X-linked (X), Y-linked (Y), and autosomal (A) genes. Rodent silent site X/A comparisons provide very different estimates from X/Y comparisons. We examine three explanations for this discrepancy: (1) statistical biases and artifacts, (2) nonneutral evolution, and (3) differences in mutation rate per germline replication. By estimating errors and using a variety of methodologies, we tentatively reject explanation 1. Our analyses of patterns of codon usage, synonymous rates, and nonsynonymous rates suggest that silent sites in rodents are evolving neutrally, and we can therefore reject explanation 2. We find both base composition and methylation differences between the different sets of chromosomes, a result consistent with explanation 3, but these differences do not appear to explain the observed discrepancies in estimates of alpha. Our finding of significantly low synonymous substitution rates in genomically imprinted genes suggests a link between hemizygous expression and an adaptive reduction in the mutation rate, which is consistent with explanation 3. Therefore our results provide circumstantial evidence in favor of the hypothesis that the discrepancies in estimates of alpha are due to differences in the mutation rate per germline replication between different parts of the genome. This explanation violates a critical assumption of the method of Miyata et al., and hence we suggest that estimates of alpha, obtained using this method, need to be treated with caution. PMID:10353908
Multichannel sparse spike inversion
NASA Astrophysics Data System (ADS)
Pereg, Deborah; Cohen, Israel; Vassiliou, Anthony A.
2017-10-01
In this paper, we address the problem of sparse multichannel seismic deconvolution. We introduce multichannel sparse spike inversion as an iterative procedure, which deconvolves the seismic data and recovers the Earth two-dimensional reflectivity image, while taking into consideration the relations between spatially neighboring traces. We demonstrate the improved performance of the proposed algorithm and its robustness to noise, compared to competitive single-channel algorithm through simulations and real seismic data examples.
Spiking neural network for recognizing spatiotemporal sequences of spikes
NASA Astrophysics Data System (ADS)
Jin, Dezhe Z.
2004-02-01
Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons.
2007-03-01
performance as a function of the exponential coefficient, the combining method, the probability of false alarm, signal-to-AWGN ratio, and signal-to...the combining method, the probability of false alarm, signal-to-AWGN ratio, and signal-to-interference ratio. The second method of SS signal...versus SNR with standard ACRE for data durations from four to thirty-two ms with the associated upper-estimate of the probability of false alarm for
NASA Technical Reports Server (NTRS)
Challa, M. S.; Natanson, G. A.; Baker, D. F.; Deutschmann, J. K.
1994-01-01
This paper describes real-time attitude determination results for the Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX), a gyroless spacecraft, using a Kalman filter/Euler equation approach denoted the real-time sequential filter (RTSF). The RTSF is an extended Kalman filter whose state vector includes the attitude quaternion and corrections to the rates, which are modeled as Markov processes with small time constants. The rate corrections impart a significant robustness to the RTSF against errors in modeling the environmental and control torques, as well as errors in the initial attitude and rates, while maintaining a small state vector. SAMPLEX flight data from various mission phases are used to demonstrate the robustness of the RTSF against a priori attitude and rate errors of up to 90 deg and 0.5 deg/sec, respectively, as well as a sensitivity of 0.0003 deg/sec in estimating rate corrections in torque computations. In contrast, it is shown that the RTSF attitude estimates without the rate corrections can degrade rapidly. RTSF advantages over single-frame attitude determination algorithms are also demonstrated through (1) substantial improvements in attitude solutions during sun-magnetic field coalignment and (2) magnetic-field-only attitude and rate estimation during the spacecraft's sun-acquisition mode. A robust magnetometer-only attitude-and-rate determination method is also developed to provide for the contingency when both sun data as well as a priori knowledge of the spacecraft state are unavailable. This method includes a deterministic algorithm used to initialize the RTSF with coarse estimates of the spacecraft attitude and rates. The combined algorithm has been found effective, yielding accuracies of 1.5 deg in attitude and 0.01 deg/sec in the rates and convergence times as little as 400 sec.
Estimating respiratory rate from FBG optical sensors by using signal quality measurement.
Yongwei Zhu; Maniyeri, Jayachandran; Fook, Victor Foo Siang; Haihong Zhang
2015-08-01
Non-intrusiveness is one of the advantages of in-bed optical sensor device for monitoring vital signs, including heart rate and respiratory rate. Estimating respiratory rate reliably using such sensors, however, is challenging, due to body movement, signal variation according to different subjects or body positions, etc. This paper presents a method for reliable respiratory rate estimation for FBG optical sensors by introducing signal quality estimation. The method estimates the quality of the signal waveform by detecting regularly repetitive patterns using proposed spectrum and cepstrum analysis. Multiple window sizes are used to cater for a wide range of target respiratory rates. Furthermore, the readings of multiple sensors are fused to derive a final respiratory rate. Experiments with 12 subjects and 2 body positions were conducted using polysomnography belt signal as groundtruth. The results demonstrated the effectiveness of the method.
Temporal pairwise spike correlations fully capture single-neuron information
Dettner, Amadeus; Münzberg, Sabrina; Tchumatchenko, Tatjana
2016-01-01
To crack the neural code and read out the information neural spikes convey, it is essential to understand how the information is coded and how much of it is available for decoding. To this end, it is indispensable to derive from first principles a minimal set of spike features containing the complete information content of a neuron. Here we present such a complete set of coding features. We show that temporal pairwise spike correlations fully determine the information conveyed by a single spiking neuron with finite temporal memory and stationary spike statistics. We reveal that interspike interval temporal correlations, which are often neglected, can significantly change the total information. Our findings provide a conceptual link between numerous disparate observations and recommend shifting the focus of future studies from addressing firing rates to addressing pairwise spike correlation functions as the primary determinants of neural information. PMID:27976717
NASA Technical Reports Server (NTRS)
Williams, R. E.; Kruger, R.
1980-01-01
Estimation procedures are described for measuring component failure rates, for comparing the failure rates of two different groups of components, and for formulating confidence intervals for testing hypotheses (based on failure rates) that the two groups perform similarly or differently. Appendix A contains an example of an analysis in which these methods are applied to investigate the characteristics of two groups of spacecraft components. The estimation procedures are adaptable to system level testing and to monitoring failure characteristics in orbit.
Microcephaly Case Fatality Rate Associated with Zika Virus Infection in Brazil: Current Estimates.
Cunha, Antonio José Ledo Alves da; de Magalhães-Barbosa, Maria Clara; Lima-Setta, Fernanda; Medronho, Roberto de Andrade; Prata-Barbosa, Arnaldo
2017-05-01
Considering the currently confirmed cases of microcephaly and related deaths associated with Zika virus in Brazil, the estimated case fatality rate is 8.3% (95% confidence interval: 7.2-9.6). However, a third of the reported cases remain under investigation. If the confirmation rates of cases and deaths are the same in the future, the estimated case fatality rate will be as high as 10.5% (95% confidence interval: 9.5-11.7).
Estimates of Erosion Rates for the Central Southern Alps, New Zealand
NASA Astrophysics Data System (ADS)
Hales, T.; Roering, J. J.
2002-12-01
The shape and scale of mountains is governed by the interaction of tectonic and isostatic forces that generate uplift and surface processes that erode and redistribute material. In several mountain ranges (e.g., the Olympics, the Himalayas) the rate at which uplift occurs is thought to be balanced by the rate of erosion, a concept known as steady state. Quantification of this linkage is made difficult by the cyclic nature of erosional processes that occur at a different timescale to processes producing rock uplift. The Southern Alps of New Zealand have been reported to be in steady state. Rock uplift has been estimated using a number of different methods including the amount of retrograde metamorphism and geobarometry. Rates of erosion have been estimated over 10 Ma using thermochronometers and at the decadal scale using suspended sediment yield data. Few estimates of erosion have been reported for intermediate timescales. The Cass valley in the central Southern Alps was glaciated until ~12 k.a. B.P., when it was isolated from the adjoining Waimakariri valley. We estimated erosion rates of 1-2 mm/yr from volume estimates of alluvial fans that developed in Cass valley following glacial retreat. These rates are similar to exhumation rates estimated using thermochronometers, implying that post-glacial erosion rates may not differ significantly from rates of erosion during glacial advances. Local variation in erosion rates within our study area correlate with changes in the mean elevation of the contributing valleys. This implies that climatic factors including mean annual temperature and precipitation, which dictate the amount and type of vegetation and time exposed to freeze/thaw cycles, may influence production rates of sediment. These data are some of the first estimates of erosion rates in the Southern Alps on an interglacial timescale. That these estimates do not differ substantially from long-term erosion rates implies that local climatic variation and rock
Effects of visual stimulation on LFPs, spikes, and LFP-spike relations in PRR.
Hwang, Eun Jung; Andersen, Richard A
2011-04-01
Local field potentials (LFPs) have shown diverse relations to the spikes across different brain areas and stimulus features, suggesting that LFP-spike relationships are highly specific to the underlying connectivity of a local network. If so, the LFP-spike relationship may vary even within one brain area under the same task condition if neurons have heterogeneous connectivity with the active input sources during the task. Here, we tested this hypothesis in the parietal reach region (PRR), which includes two distinct classes of motor goal planning neurons with different connectivity to the visual input, i.e., visuomotor neurons receive stronger visual input than motor neurons. We predicted that the visual stimulation would render both the spike response and the LFP-spike relationship different between the two neuronal subpopulations. Thus we examined how visual stimulations affect spikes, LFPs, and LFP-spike relationships in PRR by comparing their planning (delay) period activity between two conditions: with or without a visual stimulus at the reach target. Neurons were classified as visuomotor if the visual stimulation increased their firing rate, or as motor otherwise. We found that the visual stimulation increased LFP power in gamma bands >40 Hz for both classes. Moreover, confirming our prediction, the correlation between the LFP gamma power and the firing rate became higher for the visuomotor than motor neurons in the presence of visual stimulation. We conclude that LFPs vary with the stimulation condition and that the LFP-spike relationship depends on a given neuron's connectivity to the dominant input sources in a particular stimulation condition.
Evoking prescribed spike times in stochastic neurons
NASA Astrophysics Data System (ADS)
Doose, Jens; Lindner, Benjamin
2017-09-01
Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.
A computer program for estimating fish population sizes and annual production rates
Railsback, S.F.; Holcomb, B.D.; Ryon, M.G.
1989-10-01
This report documents a program that estimates fish population sizes and annual production rates in small streams from multiple-pass sampling data. A maximum weighted likelihood method is used to estimate population sizes (Carle and Strub, 1978), and a size-frequency method is used to estimate production (Garman and Waters, 1983). The program performs the following steps: (1) reads in the data and performs error checking; (2) where required, uses length-weight regression to fill in missing weights; (3) assigns length classes to the fish; (4) for each date, species, and length class, estimates the population size and its variance; (5) for each date and species, estimates the total population size and its variance; and (6) for each species, estimates the annual production rate and its variance between sampling dates selected by the user. If data from only date are used, only populations are estimated. 9 refs.
Effects of tag loss on direct estimates of population growth rate
Rotella, J.J.; Hines, J.E.
2005-01-01
The temporal symmetry approach of R. Pradel can be used with capture-recapture data to produce retrospective estimates of a population's growth rate, lambda(i), and the relative contributions to lambda(i) from different components of the population. Direct estimation of lambda(i) provides an alternative to using population projection matrices to estimate asymptotic lambda and is seeing increased use. However, the robustness of direct estimates of lambda(1) to violations of several key assumptions has not yet been investigated. Here, we consider tag loss as a possible source of bias for scenarios in which the rate of tag loss is (1) the same for all marked animals in the population and (2) a function of tag age. We computed analytic approximations of the expected values for each of the parameter estimators involved in direct estimation and used those values to calculate bias and precision for each parameter estimator. Estimates of lambda(i) were robust to homogeneous rates of tag loss. When tag loss rates varied by tag age, bias occurred for some of the sampling situations evaluated, especially those with low capture probability, a high rate of tag loss, or both. For situations with low rates of tag loss and high capture probability, bias was low and often negligible. Estimates of contributions of demographic components to lambda(i) were not robust to tag loss. Tag loss reduced the precision of all estimates because tag loss results in fewer marked animals remaining available for estimation. Clearly tag loss should be prevented if possible, and should be considered in analyses of lambda(i), but tag loss does not necessarily preclude unbiased estimation of lambda(i).
Estimating average annual percent change for disease rates without assuming constant change.
Fay, Michael P; Tiwari, Ram C; Feuer, Eric J; Zou, Zhaohui
2006-09-01
The annual percent change (APC) is often used to measure trends in disease and mortality rates, and a common estimator of this parameter uses a linear model on the log of the age-standardized rates. Under the assumption of linearity on the log scale, which is equivalent to a constant change assumption, APC can be equivalently defined in three ways as transformations of either (1) the slope of the line that runs through the log of each rate, (2) the ratio of the last rate to the first rate in the series, or (3) the geometric mean of the proportional changes in the rates over the series. When the constant change assumption fails then the first definition cannot be applied as is, while the second and third definitions unambiguously define the same parameter regardless of whether the assumption holds. We call this parameter the percent change annualized (PCA) and propose two new estimators of it. The first, the two-point estimator, uses only the first and last rates, assuming nothing about the rates in between. This estimator requires fewer assumptions and is asymptotically unbiased as the size of the population gets large, but has more variability since it uses no information from the middle rates. The second estimator is an adaptive one and equals the linear model estimator with a high probability when the rates are not significantly different from linear on the log scale, but includes fewer points if there are significant departures from that linearity. For the two-point estimator we can use confidence intervals previously developed for ratios of directly standardized rates. For the adaptive estimator, we show through simulation that the bootstrap confidence intervals give appropriate coverage.
Estimation in a discrete tail rate family of recapture sampling models
NASA Technical Reports Server (NTRS)
Gupta, Rajan; Lee, Larry D.
1990-01-01
In the context of recapture sampling design for debugging experiments the problem of estimating the error or hitting rate of the faults remaining in a system is considered. Moment estimators are derived for a family of models in which the rate parameters are assumed proportional to the tail probabilities of a discrete distribution on the positive integers. The estimators are shown to be asymptotically normal and fully efficient. Their fixed sample properties are compared, through simulation, with those of the conditional maximum likelihood estimators.
Markov models and the ensemble Kalman filter for estimation of sorption rates.
Vugrin, Eric D.; McKenna, Sean Andrew; Vugrin, Kay White
2007-09-01
Non-equilibrium sorption of contaminants in ground water systems is examined from the perspective of sorption rate estimation. A previously developed Markov transition probability model for solute transport is used in conjunction with a new conditional probability-based model of the sorption and desorption rates based on breakthrough curve data. Two models for prediction of spatially varying sorption and desorption rates along a one-dimensional streamline are developed. These models are a Markov model that utilizes conditional probabilities to determine the rates and an ensemble Kalman filter (EnKF) applied to the conditional probability method. Both approaches rely on a previously developed Markov-model of mass transfer, and both models assimilate the observed concentration data into the rate estimation at each observation time. Initial values of the rates are perturbed from the true values to form ensembles of rates and the ability of both estimation approaches to recover the true rates is examined over three different sets of perturbations. The models accurately estimate the rates when the mean of the perturbations are zero, the unbiased case. For the cases containing some bias, addition of the ensemble Kalman filter is shown to improve accuracy of the rate estimation by as much as an order of magnitude.
Optimal rain rate estimation algorithm for light and heavy rain using polarimetric measurements
NASA Astrophysics Data System (ADS)
Elmzoughi, A.; Abdelfattah, R.; Santalla Del Rio, V.; Belhadj, Z.
2011-11-01
In this paper, we propose an ameliorated physically-based rain rate estimation algorithm for semi-arid regions using the Rayleigh approximation. The proposed algorithm simultaneously uses the reflectivity and the specific differential phase to provide an accurate estimation for both small and large rain rates. In order to validate the proposed estimator, simulated polarimetric rain rate data based on a dual approach, referring to both physical and statistical models of the rain target, are used. Moreover, experimental radar data (the same as used in Matrosov et al., 2006) taken in light to moderate stratiform rainfalls with rain rates varying between 2 and 15 mm h-1 were collected as part of the GPM pilot experiment. It is shown that the proposed algorithm for rain rate estimation based on the full set of polarimetric radar measurements agree better with in situ disdrometer ones.
Sampling studies to estimate the HIV prevalence rate in female commercial sex workers.
Pascom, Ana Roberta Pati; Szwarcwald, Célia Landmann; Barbosa Júnior, Aristides
2010-01-01
We investigated sampling methods being used to estimate the HIV prevalence rate among female commercial sex workers. The studies were classified according to the adequacy or not of the sample size to estimate HIV prevalence rate and according to the sampling method (probabilistic or convenience). We identified 75 studies that estimated the HIV prevalence rate among female sex workers. Most of the studies employed convenience samples. The sample size was not adequate to estimate HIV prevalence rate in 35 studies. The use of convenience sample limits statistical inference for the whole group. It was observed that there was an increase in the number of published studies since 2005, as well as in the number of studies that used probabilistic samples. This represents a large advance in the monitoring of risk behavior practices and HIV prevalence rate in this group.
De La Mata, Nicole L.; Mao, Limin; De Wit, John; Smith, Don; Holt, Martin; Prestage, Garrett; Wilson, David P.; Petoumenos, Kathy
2016-01-01
Gay and other men who have sex with men (GMSM) are disproportionally affected by the HIV epidemic in Australia. The study objective is to combine a clinical-based cohort with a community-based surveillance system to present a broader representation of the GMSM community to determine estimates of proportions receiving antiretroviral therapy (ART) and/or with an undetectable viral load. Between 2010 and 2012, small increases were shown in ART uptake (to 70.2%) and proportions with undetectable viral load (to 62.4%). The study findings highlight the potential for significantly increasing ART uptake among HIV-positive GMSM to reduce the HIV epidemic in Australia. PMID:26166247
Direct Magnitude Estimation of Articulation Rate in Boys with Fragile X Syndrome
ERIC Educational Resources Information Center
Zajac, David J.; Harris, Adrianne A.; Roberts, Joanne E.; Martin, Gary E.
2009-01-01
Purpose: To compare the perceived articulation rate of boys with fragile X syndrome (FXS) with that of chronologically age-matched (CA) boys and to determine segmental and/or prosodic factors that account for perceived rate. Method: Ten listeners used direct magnitude estimation procedures to judge the articulation rates of 7 boys with FXS only, 5…
Estimating Infiltration Rates for a Loessal Silt Loam Using Soil Properties
M. Dean Knighton
1978-01-01
Soil properties were related to infiltration rates as measured by single-ringsteady-head infiltometers. The properties showing strong simple correlations were identified. Regression models were developed to estimate infiltration rate from several soil properties. The best model gave fair agreement to measured rates at another location.
Odor emission rate estimation of indoor industrial sources using a modified inverse modeling method.
Li, Xiang; Wang, Tingting; Sattayatewa, Chakkrid; Venkatesan, Dhesikan; Noll, Kenneth E; Pagilla, Krishna R; Moschandreas, Demetrios J
2011-08-01
Odor emission rates are commonly measured in the laboratory or occasionally estimated with inverse modeling techniques. A modified inverse modeling approach is used to estimate source emission rates inside of a postdigestion centrifuge building of a water reclamation plant. Conventionally, inverse modeling methods divide an indoor environment in zones on the basis of structural design and estimate source emission rates using models that assume homogeneous distribution of agent concentrations within a zone and experimentally determined link functions to simulate airflows among zones. The modified approach segregates zones as a function of agent distribution rather than building design and identifies near and far fields. Near-field agent concentrations do not satisfy the assumption of homogeneous odor concentrations; far-field concentrations satisfy this assumption and are the only ones used to estimate emission rates. The predictive ability of the modified inverse modeling approach was validated with measured emission rate values; the difference between corresponding estimated and measured odor emission rates is not statistically significant. Similarly, the difference between measured and estimated hydrogen sulfide emission rates is also not statistically significant. The modified inverse modeling approach is easy to perform because it uses odor and odorant field measurements instead of complex chamber emission rate measurements.
The Impact of the Rate Prior on Bayesian Estimation of Divergence Times with Multiple Loci
Dos Reis, Mario; Zhu, Tianqi; Yang, Ziheng
2014-01-01
Bayesian methods provide a powerful way to estimate species divergence times by combining information from molecular sequences with information from the fossil record. With the explosive increase of genomic data, divergence time estimation increasingly uses data of multiple loci (genes or site partitions). Widely used computer programs to estimate divergence times use independent and identically distributed (i.i.d.) priors on the substitution rates for different loci. The i.i.d. prior is problematic. As the number of loci (L) increases, the prior variance of the average rate across all loci goes to zero at the rate 1/L. As a consequence, the rate prior dominates posterior time estimates when many loci are analyzed, and if the rate prior is misspecified, the estimated divergence times will converge to wrong values with very narrow credibility intervals. Here we develop a new prior on the locus rates based on the Dirichlet distribution that corrects the problematic behavior of the i.i.d. prior. We use computer simulation and real data analysis to highlight the differences between the old and new priors. For a dataset for six primate species, we show that with the old i.i.d. prior, if the prior rate is too high (or too low), the estimated divergence times are too young (or too old), outside the bounds imposed by the fossil calibrations. In contrast, with the new Dirichlet prior, posterior time estimates are insensitive to the rate prior and are compatible with the fossil calibrations. We re-analyzed a phylogenomic data set of 36 mammal species and show that using many fossil calibrations can alleviate the adverse impact of a misspecified rate prior to some extent. We recommend the use of the new Dirichlet prior in Bayesian divergence time estimation. [Bayesian inference, divergence time, relaxed clock, rate prior, partition analysis.] PMID:24658316
An energy ratio based measure for F-wave backfiring rate estimation.
Kong, Xuan; Gozani, Shai N
2010-01-01
F-wave persistence is a frequently reported parameter for nerve conduction studies. F-wave activities are generated through backfiring of motor neurons. F-wave persistence is designed to estimate the backfiring rate. Through computational models and probability analyses, we demonstrated that the F-wave persistence definition is deficient in providing a robust and consistent estimate of the backfiring rate. We proposed an energy ratio based measure as an alternative to F-wave persistence to estimate backfiring rates. The energy ratio measure is shown to be robust to activity detection threshold and without the ceiling effect suffered by the traditional F-wave persistence.
Schwartz, Janice B
2016-10-01
To determine the potential effect of substituting glomerular filtration rate (GFR) estimates for renal clearance estimated using the Cockcroft-Gault method (CrCL-CG) to calculate direct oral anticoagulant (DOAC) dosing. Simulation and retrospective data analysis. Community, academic institution, nursing home. Noninstitutionalized individuals aged 19 to 80 from the National Health and Nutrition Examination Survey (NHANES) (2011/12) (n = 4,687) and medically stable research participants aged 25 to 105 (n = 208). Age, height, weight, sex, race, serum creatinine, CrCL-CG, and GFR (according to the Modification of Diet in Renal Disease and Chronic Kidney Disease Epidemiology Collaboration equations). Outcome measures were dosing errors if GFR were to be substituted for CrCL-CG. Renal clearance estimates according to all methods were highly correlated (P < .001), although at lower clearances, substitution of GFR estimates for CrCL-CG resulted in failure to recognize needs for dose reductions of rivaroxaban or edoxaban in 28% of NHANES subjects and 47% to 56% of research subjects. At a CrCL-CG of less than 30 mL/min, GFR estimates missed indicated dosage reductions for dabigatran in 18% to 21% of NHANES subjects and 57% to 86% of research subjects. Age and weight contributed to differences between renal clearance estimates (P < .001), but correction of GFR for body surface area (BSA) did not reduce dosing errors. At a CrCL-CG greater than 95 mL/min, edoxaban is not recommended, and GFR esimates misclassified 24% of NHANES and 39% of research subjects. Correction for BSA reduced misclassification to 7% for NHANES and 14% in research subjects. Substitution of GFR estimates for estimated CrCl can lead to failure to recognize indications for reducing DOAC dose and potentially higher bleeding rates than in randomized trials. © 2016, Copyright the Authors Journal compilation © 2016, The American Geriatrics Society.
1996-08-07
Methanol spot markets in the US Gulf Coast cooled a bit late last week from their Monday spike in the wake of a pipeline rupture and fire that shut down Lyondell Petrochemical`s Channelview, TX complex and its 248-million gal/year methanol plant. The unit resumed production last week and was expected to return to full service by August 3. Offering prices shot up at least 10% over the pre-accident level of about 50 cts/gal fob. No actual business could be confirmed at a price of more than 52 cts-53 cts/gal, however.
Doubly robust estimator for net survival rate in analyses of cancer registry data.
Komukai, Sho; Hattori, Satoshi
2017-03-01
Cancer population studies based on cancer registry databases are widely conducted to address various research questions. In general, cancer registry databases do not collect information on cause of death. The net survival rate is defined as the survival rate if a subject would not die for any causes other than cancer. This counterfactual concept is widely used for the analyses of cancer registry data. Perme, Stare, and Estève (2012) proposed a nonparametric estimator of the net survival rate under the assumption that the censoring time is independent of the survival time and covariates. Kodre and Perme (2013) proposed an inverse weighting estimator for the net survival rate under the covariate-dependent censoring. An alternative approach to estimating the net survival rate under covariate-dependent censoring is to apply a regression model for the conditional net survival rate given covariates. In this article, we propose a new estimator for the net survival rate. The proposed estimator is shown to be doubly robust in the sense that it is consistent at least one of the regression models for survival time and for censoring time. We examine the theoretical and empirical properties of our proposed estimator by asymptotic theory and simulation studies. We also apply the proposed method to cancer registry data for gastric cancer patients in Osaka, Japan.
Spiking Neurons for Analysis of Patterns
NASA Technical Reports Server (NTRS)
Huntsberger, Terrance
2008-01-01
neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.
A neural network model of reliably optimized spike transmission.
Samura, Toshikazu; Ikegaya, Yuji; Sato, Yasuomi D
2015-06-01
We studied the detailed structure of a neuronal network model in which the spontaneous spike activity is correctly optimized to match the experimental data and discuss the reliability of the optimized spike transmission. Two stochastic properties of the spontaneous activity were calculated: the spike-count rate and synchrony size. The synchrony size, expected to be an important factor for optimization of spike transmission in the network, represents a percentage of observed coactive neurons within a time bin, whose probability approximately follows a power-law. We systematically investigated how these stochastic properties could matched to those calculated from the experimental data in terms of the log-normally distributed synaptic weights between excitatory and inhibitory neurons and synaptic background activity induced by the input current noise in the network model. To ensure reliably optimized spike transmission, the synchrony size as well as spike-count rate were simultaneously optimized. This required changeably balanced log-normal distributions of synaptic weights between excitatory and inhibitory neurons and appropriately amplified synaptic background activity. Our results suggested that the inhibitory neurons with a hub-like structure driven by intensive feedback from excitatory neurons were a key factor in the simultaneous optimization of the spike-count rate and synchrony size, regardless of different spiking types between excitatory and inhibitory neurons.
The possible role of spike patterns in cortical information processing.
Tiesinga, Paul H E; Toups, J Vincent
2005-06-01
When the same visual stimulus is presented across many trials, neurons in the visual cortex receive stimulus-related synaptic inputs that are reproducible across trials (S) and inputs that are not (N). The variability of spike trains recorded in the visual cortex and their apparent lack of spike-to-spike correlations beyond that implied by firing rate fluctuations, has been taken as evidence for a low S/N ratio. A recent re-analysis of in vivo cortical data revealed evidence for spike-to-spike correlations in the form of spike patterns. We examine neural dynamics at a higher S/N in order to determine what possible role spike patterns could play in cortical information processing. In vivo-like spike patterns were obtained in model simulations. Superpositions of multiple sinusoidal driving currents were especially effective in producing stable long-lasting patterns. By applying current pulses that were either short and strong or long and weak, neurons could be made to switch from one pattern to another. Cortical neurons with similar stimulus preferences are located near each other, have similar biophysical properties and receive a large number of common synaptic inputs. Hence, recordings of a single neuron across multiple trials are usually interpreted as the response of an ensemble of these neurons during one trial. In the presence of distinct spike patterns across trials there is ambiguity in what would be the corresponding ensemble, it could consist of the same spike pattern for each neuron or a set of patterns across neurons. We found that the spiking response of a neuron receiving these ensemble inputs was determined by the spike-pattern composition, which, in turn, could be modulated dynamically as a means for cortical information processing.
Solving Constraint Satisfaction Problems with Networks of Spiking Neurons
Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang
2016-01-01
Network of neurons in the brain apply—unlike processors in our current generation of computer hardware—an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. PMID:27065785
Finding the event structure of neuronal spike trains.
Toups, J Vincent; Fellous, Jean-Marc; Thomas, Peter J; Sejnowski, Terrence J; Tiesinga, Paul H
2011-09-01
Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004 ). Finding events in data with realistic spiking statistics is challenging because events belonging to different spike patterns may overlap. We propose a method for finding spiking events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.
Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.
Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang
2016-01-01
Network of neurons in the brain apply-unlike processors in our current generation of computer hardware-an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling.
Watrous, Matthew G.; Delmore, James E.; Hague, Robert K.; ...
2015-08-27
Four of the radioactive xenon isotopes (131mXe, 133mXe, 133Xe and 135Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The Internationalmore » Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This study focuses on Idaho National Laboratory's capability to produce three of the xenon isotopes in pure form and the use of the four xenon isotopes in various combinations to produce radioactive xenon spiked air samples that could be subsequently distributed to participating facilities.« less
Type I Error Rates and Power Estimates of Selected Parametric and Nonparametric Tests of Scale.
ERIC Educational Resources Information Center
Olejnik, Stephen F.; Algina, James
1987-01-01
Estimated Type I Error rates and power are reported for the Brown-Forsythe, O'Brien, Klotz, and Siegal-Tukey procedures. The effect of aligning the data using deviations from group means or group medians is investigated. (RB)
Type I Error Rates and Power Estimates of Selected Parametric and Nonparametric Tests of Scale.
ERIC Educational Resources Information Center
Olejnik, Stephen F.; Algina, James
1987-01-01
Estimated Type I Error rates and power are reported for the Brown-Forsythe, O'Brien, Klotz, and Siegal-Tukey procedures. The effect of aligning the data using deviations from group means or group medians is investigated. (RB)
Balanced synaptic input shapes the correlation between neural spike trains.
Litwin-Kumar, Ashok; Oswald, Anne-Marie M; Urban, Nathaniel N; Doiron, Brent
2011-12-01
Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states.
Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains
Litwin-Kumar, Ashok; Oswald, Anne-Marie M.; Urban, Nathaniel N.; Doiron, Brent
2011-01-01
Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states. PMID:22215995
Estimate of the Time Rate of Entropy Dissipation for Systems of Conservation Laws
NASA Astrophysics Data System (ADS)
Sever, Michael
1996-09-01
A priori estimates for weak solutions of nonlinear systems of conservation laws remain in short supply. In this note we obtain an estimate of the rate of total entropy dissipation for initial/boundary value problems for such systems, of any dimension and in any number of space variables. The essential assumptions made are those of a strictly convex entropy density, anL∞estimate on the solution, and initial data of "bounded variation" as described here.
Estimates of bacterial growth from changes in uptake rates and biomass.
Kirchman, D; Ducklow, H; Mitchell, R
1982-01-01
Rates of nucleic acid synthesis have been used to examine microbiol growth in natural waters. These rates are calculated from the incorporation of [3H]adenine and [3H]thymidine for RNA and DNA syntheses, respectively. Several additional biochemical parameters must be measured or taken from the literature to estimate growth rates from the incorporation of the tritiated compounds. We propose a simple method of estimating a conversion factor which obviates measuring these biochemical parameters. The change in bacterial abundance and incorporation rates of [3H]thymidine was measured in samples from three environments. The incorporation of exogenous [3H]thymidine was closely coupled with growth and cell division as estimated from the increase in bacterial biomass. Analysis of the changes in incorporation rates and initial bacterial abundance yielded a conversion factor for calculating bacterial production rates from incorporation rates. Furthermore, the growth rate of only those bacteria incorporating the compound can be estimated. The data analysis and experimental design can be used to estimate the proportion of nondividing cells and to examine changes in cell volumes. PMID:6760812
Weiss, Eric H.; Sayadi, Omid; Ramaswamy, Priya; Merchant, Faisal M.; Sajja, Naveen; Foley, Lori; Laferriere, Shawna
2014-01-01
It is well-known that respiratory activity influences electrocardiographic (ECG) morphology. In this article we present a new algorithm for the extraction of respiratory rate from either intracardiac or body surface electrograms. The algorithm optimizes selection of ECG leads for respiratory analysis, as validated in a swine model. The algorithm estimates the respiratory rate from any two ECG leads by finding the power spectral peak of the derived ratio of the estimated root-mean-squared amplitude of the QRS complexes on a beat-by-beat basis across a 32-beat window and automatically selects the lead combination with the highest power spectral signal-to-noise ratio. In 12 mechanically ventilated swine, we collected intracardiac electrograms from catheters in the right ventricle, coronary sinus, left ventricle, and epicardial surface, as well as body surface electrograms, while the ventilation rate was varied between 7 and 13 breaths/min at tidal volumes of 500 and 750 ml. We found excellent agreement between the estimated and true respiratory rate for right ventricular (R2 = 0.97), coronary sinus (R2 = 0.96), left ventricular (R2 = 0.96), and epicardial (R2 = 0.97) intracardiac leads referenced to surface lead ECGII. When applied to intracardiac right ventricular-coronary sinus bipolar leads, the algorithm exhibited an accuracy of 99.1% (R2 = 0.97). When applied to 12-lead body surface ECGs collected in 4 swine, the algorithm exhibited an accuracy of 100% (R2 = 0.93). In conclusion, the proposed algorithm provides an accurate estimation of the respiratory rate using either intracardiac or body surface signals without the need for additional hardware. PMID:24858847
Weiss, Eric H; Sayadi, Omid; Ramaswamy, Priya; Merchant, Faisal M; Sajja, Naveen; Foley, Lori; Laferriere, Shawna; Armoundas, Antonis A
2014-08-01
It is well-known that respiratory activity influences electrocardiographic (ECG) morphology. In this article we present a new algorithm for the extraction of respiratory rate from either intracardiac or body surface electrograms. The algorithm optimizes selection of ECG leads for respiratory analysis, as validated in a swine model. The algorithm estimates the respiratory rate from any two ECG leads by finding the power spectral peak of the derived ratio of the estimated root-mean-squared amplitude of the QRS complexes on a beat-by-beat basis across a 32-beat window and automatically selects the lead combination with the highest power spectral signal-to-noise ratio. In 12 mechanically ventilated swine, we collected intracardiac electrograms from catheters in the right ventricle, coronary sinus, left ventricle, and epicardial surface, as well as body surface electrograms, while the ventilation rate was varied between 7 and 13 breaths/min at tidal volumes of 500 and 750 ml. We found excellent agreement between the estimated and true respiratory rate for right ventricular (R(2) = 0.97), coronary sinus (R(2) = 0.96), left ventricular (R(2) = 0.96), and epicardial (R(2) = 0.97) intracardiac leads referenced to surface lead ECGII. When applied to intracardiac right ventricular-coronary sinus bipolar leads, the algorithm exhibited an accuracy of 99.1% (R(2) = 0.97). When applied to 12-lead body surface ECGs collected in 4 swine, the algorithm exhibited an accuracy of 100% (R(2) = 0.93). In conclusion, the proposed algorithm provides an accurate estimation of the respiratory rate using either intracardiac or body surface signals without the need for additional hardware.
Temporal and geographic estimates of survival and recovery rates for the mallard, 1950 through 1985
Chu, D.S.; Hestbeck, J.B.
1989-01-01
Estimates of survival and recovery rates and the corresponding sample variances and covariances were made for mallards (Anas platyrhychos) banded before the hunting season for the period 1950-85. Estimates were made for adults and young, males and females, for as many banding reference areas as possible using standard band-recovery methods.
An estimator for the relative entropy rate of path measures for stochastic differential equations
NASA Astrophysics Data System (ADS)
Opper, Manfred
2017-02-01
We address the problem of estimating the relative entropy rate (RER) for two stochastic processes described by stochastic differential equations. For the case where the drift of one process is known analytically, but one has only observations from the second process, we use a variational bound on the RER to construct an estimator.
An estimating formula for ion-atom association rates in gases
NASA Technical Reports Server (NTRS)
Chatterjee, B. K.; Johnsen, R.
1990-01-01
A simple estimating formula is derived for rate coefficients of three-body ion atom association in gases and compare its predictions to experimental data on ion association and three-body radiative charge transfer reactions of singly- and doubly-charged rare-gas ions. The formula appears to reproduce most experimental data quite well. It may be useful for estimating the rates of reactions that have not been studied in the laboratory.
Alkema, Leontine; Raftery, Adrian E.; Gerland, Patrick; Clark, Samuel J.; Pelletier, François
2013-01-01
Background Estimating the total fertility rate is challenging for many developing countries because of limited data and varying data quality. A standardized, reproducible approach to produce estimates that include an uncertainty assessment is desired. Methods We develop a method to estimate and assess uncertainty in the total fertility rate over time, based on multiple imperfect observations from different data sources, including surveys and censuses. We take account of measurement error in observations by decomposing it into bias and variance, and assess both by linear regression on a variety of data quality covariates. We estimate the total fertility rate using a local smoother, and assess uncertainty using the weighted likelihood bootstrap. Results We apply our method to data from seven countries in West Africa and construct estimates and uncertainty intervals for the total fertility rate. Based on cross-validation exercises, we find that accounting for differences in data quality between observations gives better calibrated confidence intervals and reduces bias. Conclusions When working with multiple imperfect observations from different data sources to estimate the total fertility rate, or demographic indicators in general, potential biases and differences in error variance should be taken into account to improve the estimates and their uncertainty assessment. PMID:24273449
Mathewson, P D; Spehar, S N; Meijaard, E; Nardiyono; Purnomo; Sasmirul, A; Sudiyanto; Oman; Sulhnudin; Jasary; Jumali; Marshall, A J
2008-01-01
An accurate estimate for orangutan nest decay time is a crucial factor in commonly used methods for estimating orangutan population size. Decay rates are known to vary, but the decay process and, thus, the temporal and spatial variation in decay time are poorly understood. We used established line-transect methodology to survey orangutan nests in a lowland forest in East Kalimantan, Indonesia, and monitored the decay of 663 nests over 20 months. Using Markov chain analysis we calculated a decay time of 602 days, which is significantly longer than times found in other studies. Based on this, we recalculated the orangutan density estimate for a site in East Kalimantan; the resulting density is much lower than previous estimates (previous estimates were 3-8 times higher than our recalculated density). Our data suggest that short-term studies where decay times are determined using matrix mathematics may produce unreliable decay times. Our findings have implications for other parts of the orangutan range where population estimates are based on potentially unreliable nest decay rate estimates, and we recommend that for various parts of the orangutan range census estimates be reexamined. Considering the high variation in decay rates there is a need to move away from using single-number decay time estimates and, preferably, to test methods that do not rely on nest decay times as alternatives for rapid assessments of orangutan habitat for conservation in Borneo.
Decker, Scott L; Schneider, W Joel; Hale, James B
2012-01-01
Neuropsychologists frequently rely on a battery of neuropsychological tests which are normally distributed to determine impaired functioning. The statistical likelihood of Type I error in clinical decision-making is in part determined by the base rate of normative individuals obtaining atypical performance on neuropsychological tests. Base rates are most accurately obtained by co-normed measures, but this is rarely accomplished in neuropsychological testing. Several statistical methods have been proposed to estimate base rates for tests that are not co-normed. This study compared two statistical approaches (binomial and Monte Carlo models) used to estimate the base rates for flexible test batteries. The two approaches were compared against empirically derived base rates for a multitest co-normed battery of cognitive measures. Estimates were compared across a variety of conditions including age and different α levels (N =3,356). Monte Carlo R(2) estimates ranged from .980 to .997 across five different age groups, indicating a good fit. In contrast, the binomial model fit estimates ranged from 0.387 to 0.646. Results confirm that the binomial model is insufficient for estimating base rates because it does not take into account correlations among measures in a multitest battery. Although the Monte Carlo model produced more accurate results, minor biases occurred that are likely due to skewess and kurtosis of test variables. Implications for future research and applied practice are discussed. © The Author 2011. Published by Oxford University Press. All rights reserved.
Estimating survival rates with time series of standing age-structure data
Udevitz, Mark S.; Gogan, Peter J.
2014-01-01
It has long been recognized that age-structure data contain useful information for assessing the status and dynamics of wildlife populations. For example, age-specific survival rates can be estimated with just a single sample from the age distribution of a stable, stationary population. For a population that is not stable, age-specific survival rates can be estimated using techniques such as inverse methods that combine time series of age-structure data with other demographic data. However, estimation of survival rates using these methods typically requires numerical optimization, a relatively long time series of data, and smoothing or other constraints to provide useful estimates. We developed general models for possibly unstable populations that combine time series of age-structure data with other demographic data to provide explicit maximum likelihood estimators of age-specific survival rates with as few as two years of data. As an example, we applied these methods to estimate survival rates for female bison (Bison bison) in Yellowstone National Park, USA. This approach provides a simple tool for monitoring survival rates based on age-structure data.
Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays
Muthmann, Jens-Oliver; Amin, Hayder; Sernagor, Evelyne; Maccione, Alessandro; Panas, Dagmara; Berdondini, Luca; Bhalla, Upinder S.; Hennig, Matthias H.
2015-01-01
An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits. PMID:26733859
Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays.
Muthmann, Jens-Oliver; Amin, Hayder; Sernagor, Evelyne; Maccione, Alessandro; Panas, Dagmara; Berdondini, Luca; Bhalla, Upinder S; Hennig, Matthias H
2015-01-01
An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits.
Filter based phase distortions in extracellular spikes.
Yael, Dorin; Bar-Gad, Izhar
2017-01-01
Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results.
Filter based phase distortions in extracellular spikes
Yael, Dorin
2017-01-01
Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results. PMID:28358895
Mullan, Patrick; Kanzler, Christoph M; Lorch, Benedikt; Schroeder, Lea; Winkler, Ludwig; Laich, Larissa; Riedel, Frederik; Richer, Robert; Luckner, Christoph; Leutheuser, Heike; Eskofier, Bjoern M; Pasluosta, Cristian
2015-01-01
Photoplethysmography (PPG) is a non-invasive, inexpensive and unobtrusive method to achieve heart rate monitoring during physical exercises. Motion artifacts during exercise challenge the heart rate estimation from wrist-type PPG signals. This paper presents a methodology to overcome these limitation by incorporating acceleration information. The proposed algorithm consisted of four stages: (1) A wavelet based denoising, (2) an acceleration based denoising, (3) a frequency based approach to estimate the heart rate followed by (4) a postprocessing step. Experiments with different movement types such as running and rehabilitation exercises were used for algorithm design and development. Evaluation of our heart rate estimation showed that a mean absolute error 1.96 bpm (beats per minute) with standard deviation of 2.86 bpm and a correlation of 0.98 was achieved with our method. These findings suggest that the proposed methodology is robust to motion artifacts and is therefore applicable for heart rate monitoring during sports and rehabilitation.
Busquets-Vass, Geraldine; Newsome, Seth D; Calambokidis, John; Serra-Valente, Gabriela; Jacobsen, Jeff K; Aguíñiga-García, Sergio; Gendron, Diane
2017-01-01
Stable isotope analysis in mysticete skin and baleen plates has been repeatedly used to assess diet and movement patterns. Accurate interpretation of isotope data depends on understanding isotopic incorporation rates for metabolically active tissues and growth rates for metabolically inert tissues. The aim of this research was to estimate isotopic incorporation rates in blue whale skin and baleen growth rates by using natural gradients in baseline isotope values between oceanic regions. Nitrogen (δ15N) and carbon (δ13C) isotope values of blue whale skin and potential prey were analyzed from three foraging zones (Gulf of California, California Current System, and Costa Rica Dome) in the northeast Pacific from 1996-2015. We also measured δ15N and δ13C values along the lengths of baleen plates collected from six blue whales stranded in the 1980s and 2000s. Skin was separated into three strata: basale, externum, and sloughed skin. A mean (±SD) skin isotopic incorporation rate of 163±91 days was estimated by fitting a generalized additive model of the seasonal trend in δ15N values of skin strata collected in the Gulf of California and the California Current System. A mean (±SD) baleen growth rate of 15.5±2.2 cm y-1 was estimated by using seasonal oscillations in δ15N values from three whales. These oscillations also showed that individual whales have a high fidelity to distinct foraging zones in the northeast Pacific across years. The absence of oscillations in δ15N values of baleen sub-samples from three male whales suggests these individuals remained within a specific zone for several years prior to death. δ13C values of both whale tissues (skin and baleen) and potential prey were not distinct among foraging zones. Our results highlight the importance of considering tissue isotopic incorporation and growth rates when studying migratory mysticetes and provide new insights into the individual movement strategies of blue whales.
Rayleigh--Taylor spike evaporation
Schappert, G. T.; Batha, S. H.; Klare, K. A.; Hollowell, D. E.; Mason, R. J.
2001-09-01
Laser-based experiments have shown that Rayleigh--Taylor (RT) growth in thin, perturbed copper foils leads to a phase dominated by narrow spikes between thin bubbles. These experiments were well modeled and diagnosed until this '' spike'' phase, but not into this spike phase. Experiments were designed, modeled, and performed on the OMEGA laser [T. R. Boehly, D. L. Brown, R. S. Craxton , Opt. Commun. 133, 495 (1997)] to study the late-time spike phase. To simulate the conditions and evolution of late time RT, a copper target was fabricated consisting of a series of thin ridges (spikes in cross section) 150 {mu}m apart on a thin flat copper backing. The target was placed on the side of a scale-1.2 hohlraum with the ridges pointing into the hohlraum, which was heated to 190 eV. Side-on radiography imaged the evolution of the ridges and flat copper backing into the typical RT bubble and spike structure including the '' mushroom-like feet'' on the tips of the spikes. RAGE computer models [R. M. Baltrusaitis, M. L. Gittings, R. P. Weaver, R. F. Benjamin, and J. M. Budzinski, Phys. Fluids 8, 2471 (1996)] show the formation of the '' mushrooms,'' as well as how the backing material converges to lengthen the spike. The computer predictions of evolving spike and bubble lengths match measurements fairly well for the thicker backing targets but not for the thinner backings.
Estimating mortality rates of adult fish from entrainment through the propellers of river towboats
Gutreuter, S.; Dettmers, J.M.; Wahl, David H.
2003-01-01
We developed a method to estimate mortality rates of adult fish caused by entrainment through the propellers of commercial towboats operating in river channels. The method combines trawling while following towboats (to recover a fraction of the kills) and application of a hydrodynamic model of diffusion (to estimate the fraction of the total kills collected in the trawls). The sampling problem is unusual and required quantifying relatively rare events. We first examined key statistical properties of the entrainment mortality rate estimators using Monte Carlo simulation, which demonstrated that a design-based estimator and a new ad hoc estimator are both unbiased and converge to the true value as the sample size becomes large. Next, we estimated the entrainment mortality rates of adult fishes in Pool 26 of the Mississippi River and the Alton Pool of the Illinois River, where we observed kills that we attributed to entrainment. Our estimates of entrainment mortality rates were 2.52 fish/km of towboat travel (80% confidence interval, 1.00-6.09 fish/km) for gizzard shad Dorosoma cepedianum, 0.13 fish/km (0.00-0.41) for skipjack herring Alosa chrysochloris, and 0.53 fish/km (0.00-1.33) for both shovelnose sturgeon Scaphirhynchus platorynchus and smallmouth buffalo Ictiobus bubalus. Our approach applies more broadly to commercial vessels operating in confined channels, including other large rivers and intracoastal waterways.
SPIKY: a graphical user interface for monitoring spike train synchrony.
Kreuz, Thomas; Mulansky, Mario; Bozanic, Nebojsa
2015-05-01
Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels.
SPIKY: a graphical user interface for monitoring spike train synchrony
Mulansky, Mario; Bozanic, Nebojsa
2015-01-01
Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. PMID:25744888
Karakas, Filiz; Imamoglu, Ipek
2017-04-01
This study aims to estimate anaerobic debromination rate constants (km) of PBDE pathways using previously reported laboratory soil data. km values of pathways are estimated by modifying a previously developed model as Anaerobic Dehalogenation Model. Debromination activities published in the literature in terms of bromine substitutions as well as specific microorganisms and their combinations are used for identification of pathways. The range of estimated km values is between 0.0003 and 0.0241 d(-1). The median and maximum of km values are found to be comparable to the few available biologically confirmed rate constants published in the literature. The estimated km values can be used as input to numerical fate and transport models for a better and more detailed investigation of the fate of individual PBDEs in contaminated sediments. Various remediation scenarios such as monitored natural attenuation or bioremediation with bioaugmentation can be handled in a more quantitative manner with the help of km estimated in this study.
Zollanvari, Amin; Genton, Marc G
2013-08-01
We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.
Mortensen, Stig B; Klim, Søren; Dammann, Bernd; Kristensen, Niels R; Madsen, Henrik; Overgaard, Rune V
2007-10-01
The non-linear mixed-effects model based on stochastic differential equations (SDEs) provides an attractive residual error model, that is able to handle serially correlated residuals typically arising from structural mis-specification of the true underlying model. The use of SDEs also opens up for new tools for model development and easily allows for tracking of unknown inputs and parameters over time. An algorithm for maximum likelihood estimation of the model has earlier been proposed, and the present paper presents the first general implementation of this algorithm. The implementation is done in Matlab and also demonstrates the use of parallel computing for improved estimation times. The use of the implementation is illustrated by two examples of application which focus on the ability of the model to estimate unknown inputs facilitated by the extension to SDEs. The first application is a deconvolution-type estimation of the insulin secretion rate based on a linear two-compartment model for C-peptide measurements. In the second application the model is extended to also give an estimate of the time varying liver extraction based on both C-peptide and insulin measurements.
Use of Pyranometers to Estimate PV Module Degradation Rates in the Field
Vignola, Frank; Peterson, Josh; Kessler, Rich; Mavromatakis, Fotis; Dooraghi, Mike; Sengupta, Manajit
2016-06-05
This poster provides an overview of a methodology that uses relative measurements to estimate the degradation rates of PV modules in the field. The importance of calibration and cleaning is illustrated. The number of years of field measurements needed to measure degradation rates with data from the field is cut in half using relative comparisons.
Use of Pyranometers to Estimate PV Module Degradation Rates in the Field
Vignola, Frank; Peterson, Josh; Kessler, Rich; Mavromatakis, Fotis; Dooraghi, Mike; Sengupta, Manajit
2016-11-21
Methodology is described that uses relative measurements to estimate the degradation rates of PV modules in the field. The importance of calibration and cleaning is discussed. The number of years of field measurements needed to measure degradation rates with data from the field is cut in half using relative comparisons.
Use of Pyranometers to Estimate PV Module Degradation Rates in the Field: Preprint
Vignola, Frank; Peterson, Josh; Kessler, Rich; Mavromatakis, Fotis; Dooraghi, Mike; Sengupta, Manajit
2016-08-01
This paper describes a methodology that uses relative measurements to estimate the degradation rates of PV modules in the field. The importance of calibration and cleaning is illustrated. The number of years of field measurements needed to measure degradation rates with data from the field is cut in half using relative comparisons.
Estimating wildland fire rate of spread in a spatially nonuniform environment
Francis M Fujioka
1985-01-01
Estimating rate of fire spread is a key element in planning for effective fire control. Land managers use the Rothermel spread model, but the model assumptions are violated when fuel, weather, and topography are nonuniform. This paper compares three averaging techniques--arithmetic mean of spread rates, spread based on mean fuel conditions, and harmonic mean of spread...
Estimating inbreeding rates in natural populations: addressing the problem of incomplete pedigrees
Mark P. Miller; Susan M. Haig; Jonathan D. Ballou; Ashley Steel
2017-01-01
Understanding and estimating inbreeding is essential for managing threatened and endangered wildlife populations. However, determination of inbreeding rates in natural populations is confounded by incomplete parentage information. We present an approach for quantifying inbreeding rates for populations with incomplete parentage information. The approach exploits...
Toni Antikainen; Anti Rohumaa; Christopher G. Hunt; Mari Levirinne; Mark Hughes
2015-01-01
In plywood production, human operators find it difficult to precisely monitor the spread rate of adhesive in real-time. In this study, macroscopic fluorescence was used to estimate spread rate (SR) of urea formaldehyde adhesive on birch (Betula pendula Roth) veneer. This method could be an option when developing automated real-time SR measurement for...
Functional connectivity among spike trains in neural assemblies during rat working memory task.
Xie, Jiacun; Bai, Wenwen; Liu, Tiaotiao; Tian, Xin
2014-11-01
Working memory refers to a brain system that provides temporary storage to manipulate information for complex cognitive tasks. As the brain is a more complex, dynamic and interwoven network of connections and interactions, the questions raised here: how to investigate the mechanism of working memory from the view of functional connectivity in brain network? How to present most characteristic features of functional connectivity in a low-dimensional network? To address these questions, we recorded the spike trains in prefrontal cortex with multi-electrodes when rats performed a working memory task in Y-maze. The functional connectivity matrix among spike trains was calculated via maximum likelihood estimation (MLE). The average connectivity value Cc, mean of the matrix, was calculated and used to describe connectivity strength quantitatively. The spike network was constructed by the functional connectivity matrix. The information transfer efficiency Eglob was calculated and used to present the features of the network. In order to establish a low-dimensional spike network, the active neurons with higher firing rates than average rate were selected based on sparse coding. The results show that the connectivity Cc and the network transfer efficiency Eglob vaired with time during the task. The maximum values of Cc and Eglob were prior to the working memory behavior reference point. Comparing with the results in the original network, the feature network could present more characteristic features of functional connectivity.
Watrous, Matthew G.; Delmore, James E.; Hague, Robert K.; Houghton, Tracy P.; Jenson, Douglas D.; Mann, Nick R.
2015-08-27
Four of the radioactive xenon isotopes (^{131m}Xe, ^{133m}Xe, ^{133}Xe and ^{135}Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The International Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This study focuses on Idaho National Laboratory's capability to produce three of the xenon isotopes in pure form and the use of the four xenon isotopes in various combinations to produce radioactive xenon spiked air samples that could be subsequently distributed to participating facilities.
Using Time-Structured Data to Estimate Evolutionary Rates of Double-Stranded DNA Viruses
Firth, Cadhla; Kitchen, Andrew; Shapiro, Beth; Suchard, Marc A.; Holmes, Edward C.; Rambaut, Andrew
2010-01-01
Double-stranded (ds) DNA viruses are often described as evolving through long-term codivergent associations with their hosts, a pattern that is expected to be associated with low rates of nucleotide substitution. However, the hypothesis of codivergence between dsDNA viruses and their hosts has rarely been rigorously tested, even though the vast majority of nucleotide substitution rate estimates for dsDNA viruses are based upon this assumption. It is therefore important to estimate the evolutionary rates of dsDNA viruses independent of the assumption of host-virus codivergence. Here, we explore the use of temporally structured sequence data within a Bayesian framework to estimate the evolutionary rates for seven human dsDNA viruses, including variola virus (VARV) (the causative agent of smallpox) and herpes simplex virus-1. Our analyses reveal that although the VARV genome is likely to evolve at a rate of approximately 1 × 10−5 substitutions/site/year and hence approaching that of many RNA viruses, the evolutionary rates of many other dsDNA viruses remain problematic to estimate. Synthetic data sets were constructed to inform our interpretation of the substitution rates estimated for these dsDNA viruses and the analysis of these demonstrated that given a sequence data set of appropriate length and sampling depth, it is possible to use time-structured analyses to estimate the substitution rates of many dsDNA viruses independently from the assumption of host-virus codivergence. Finally, the discovery that some dsDNA viruses may evolve at rates approaching those of RNA viruses has important implications for our understanding of the long-term evolutionary history and emergence potential of this major group of viruses. PMID:20363828
Hemmelgarn, Brenda R; Zhang, Jianguo; Manns, Braden J; James, Matthew T; Quinn, Robert R; Ravani, Pietro; Klarenbach, Scott W; Culleton, Bruce F; Krause, Richard; Thorlacius, Laurel; Jain, Arsh K; Tonelli, Marcello
2010-03-24
Laboratory reporting of estimated glomerular filtration rate (GFR) has been widely implemented, with limited evaluation. To examine trends in nephrologist visits and health care resource use before and after estimated GFR reporting. Community-based cohort study (N = 1,135,968) with time-series analysis. Participants were identified from a laboratory registry in Alberta, Canada, and followed up from May 15, 2003, to March 14, 2007 (with estimated GFR reporting implemented October 15, 2004). Nephrologist visits and patient management. Following estimated GFR reporting, the rate of first outpatient visits to a nephrologist for patients with chronic kidney disease (CKD; estimated GFR <60 mL/min/1.73 m(2)) increased by 17.5 (95% confidence interval [CI], 16.5-18.6) visits per 10,000 CKD patients per month, corresponding to a relative increase from baseline of 68.4% (95% CI, 65.7%-71.2%). There was no association between estimated GFR reporting and rate of first nephrologist visit among patients without CKD. Among patients with an estimated GFR of less than 30 mL/min/1.73 m(2), the rate of first nephrologist visits increased by 134.4 (95% CI, 60.0-208.7) visits per 10,000 patients per month. This increase was predominantly seen in women, patients aged 46 to 65 years as well as those aged 86 years or older, and those with hypertension, diabetes, and comorbidity. Reporting of estimated GFR was not associated with increased rates of internal medicine or general practitioner visits or increased use of angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers among patients with CKD and proteinuria or the subgroup limited to patients with diabetes. Reporting of estimated GFR was associated with an increase in first nephrologist visits, particularly among patients with more severe kidney dysfunction, women, middle-aged and very elderly patients, and those with comorbidities. Any effect on outcomes remains to be shown.
Implementing Signature Neural Networks with Spiking Neurons
Carrillo-Medina, José Luis; Latorre, Roberto
2016-01-01
Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the
Implementing Signature Neural Networks with Spiking Neurons.
Carrillo-Medina, José Luis; Latorre, Roberto
2016-01-01
Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence
Lamb, Edmund J; Wood, Joanna; Stowe, Helen J; O'Riordan, Shelagh E; Webb, Michelle C; Dalton, R Neil
2005-01-01
It is recommended that measurement of serum creatinine should be supplemented with a creatinine-based estimation of glomerular filtration rate (GFR). The influence of creatinine methodology on these estimates is not always appreciated. We have studied differences in creatinine methods and their influence on GFR estimation specifically in older people. In all, 46 older patients (mean age 80 y, range 69-92 y) with predominantly mild or moderate kidney disease were studied. Serum creatinine was measured using a rate Jaffe method and two different enzymatic methods. Isotope dilution mass spectrometry served as the reference creatinine method. GFR was estimated using both the Modification of Diet in Renal Disease (MDRD) and Cockcroft and Gault formulae: a 51Cr-EDTA GFR estimation served as the reference GFR method. Both enzymatic methods produced creatinine results that were significantly different (P<0.001) from the reference method. The Jaffe method over- and underestimated creatinine at low and high concentrations, respectively. The most likely explanation for these differences relates to standardization of the assays. Irrespective of creatinine method, the Cockroft and Gault formula tended to underestimate GFR, and the MDRD formula to overestimate GFR. Use of the differing creatinine methods to estimate GFR produced predictable biases of the estimate, with mean GFR estimates varying by 14% across the creatinine methods. Estimates of GFR depend critically upon the accuracy and precision of the creatinine measurement used in their calculation.
Application of Statistical Methods of Rain Rate Estimation to Data From The TRMM Precipitation Radar
NASA Technical Reports Server (NTRS)
Meneghini, R.; Jones, J. A.; Iguchi, T.; Okamoto, K.; Liao, L.; Busalacchi, Antonio J. (Technical Monitor)
2000-01-01
The TRMM Precipitation Radar is well suited to statistical methods in that the measurements over any given region are sparsely sampled in time. Moreover, the instantaneous rain rate estimates are often of limited accuracy at high rain rates because of attenuation effects and at light rain rates because of receiver sensitivity. For the estimation of the time-averaged rain characteristics over an area both errors are relevant. By enlarging the space-time region over which the data are collected, the sampling error can be reduced. However. the bias and distortion of the estimated rain distribution generally will remain if estimates at the high and low rain rates are not corrected. In this paper we use the TRMM PR data to investigate the behavior of 2 statistical methods the purpose of which is to estimate the rain rate over large space-time domains. Examination of large-scale rain characteristics provides a useful starting point. The high correlation between the mean and standard deviation of rain rate implies that the conditional distribution of this quantity can be approximated by a one-parameter distribution. This property is used to explore the behavior of the area-time-integral (ATI) methods where fractional area above a threshold is related to the mean rain rate. In the usual application of the ATI method a correlation is established between these quantities. However, if a particular form of the rain rate distribution is assumed and if the ratio of the mean to standard deviation is known, then not only the mean but the full distribution can be extracted from a measurement of fractional area above a threshold. The second method is an extension of this idea where the distribution is estimated from data over a range of rain rates chosen in an intermediate range where the effects of attenuation and poor sensitivity can be neglected. The advantage of estimating the distribution itself rather than the mean value is that it yields the fraction of rain contributed by
Upper-limit mutation rate estimation for a plant RNA virus
Sanjuán, Rafael; Agudelo-Romero, Patricia; Elena, Santiago F.
2009-01-01
It is generally accepted that mutation rates of RNA viruses are inherently high due to the lack of proofreading mechanisms. However, direct estimates of mutation rate are surprisingly scarce, in particular for plant viruses. Here, based on the analysis of in vivo mutation frequencies in tobacco etch virus, we calculate an upper-bound mutation rate estimation of 3×10−5 per site and per round of replication; a value which turns out to be undistinguishable from the methodological error. Nonetheless, the value is barely on the lower side of the range accepted for RNA viruses, although in good agreement with the only direct estimate obtained for other plant viruses. These observations suggest that, perhaps, differences in the selective pressures operating during plant virus evolution may have driven their mutation rates towards values lower than those characteristic of other RNA viruses infecting bacteria or animals. PMID:19324646
ESTIMATION OF FAILURE RATES OF DIGITAL COMPONENTS USING A HIERARCHICAL BAYESIAN METHOD.
YUE, M.; CHU, T.L.
2006-01-30
One of the greatest challenges in evaluating reliability of digital I&C systems is how to obtain better failure rate estimates of digital components. A common practice of the digital component failure rate estimation is attempting to use empirical formulae to capture the impacts of various factors on the failure rates. The applicability of an empirical formula is questionable because it is not based on laws of physics and requires good data, which is scarce in general. In this study, the concept of population variability of the Hierarchical Bayesian Method (HBM) is applied to estimating the failure rate of a digital component using available data. Markov Chain Monte Carlo (MCMC) simulation is used to implement the HBM. Results are analyzed and compared by selecting different distribution types and priors distributions. Inspired by the sensitivity calculations and based on review of analytic derivations, it seems reasonable to suggest avoiding the use of gamma distribution in two-stage Bayesian analysis and HBM analysis.
Evaluation and refinement of leak-rate estimation models. Revision 1
Paul, D.D.; Ahmad, J.; Scott, P.M.; Flanigan, L.F.; Wilkowski, G.M.
1994-06-01
Leak-rate estimation models are important elements in developing a leak-beforebreak methodology in piping integrity and safety analyses. Existing thermalhydraulic and crack-opening-area models used in current leak-rate estimations have been incorporated into a single computer code for leak-rate estimation. The code is called SQUIRT, which stands for Seepage Quantification of Upsets In Reactor Tubes. The SQUIRT program has been validated by comparing its thermalhydraulic predictions with the limited experimental data that have been published on two-phase flow through slits and cracks, and by comparing its crack-opening-area predictions with data from the Degraded Piping Program. In addition, leak-rate experiments were conducted to obtain validation data for a circumferential fatigue crack in a carbon steel pipe girth weld.
Markov Models and the Ensemble Kalman Filter for Estimation of Sorption Rates
NASA Astrophysics Data System (ADS)
Vugrin, E. D.; McKenna, S. A.; White Vugrin, K.
2007-12-01
Non-equilibrium sorption of contaminants in ground water systems is examined from the perspective of sorption rate estimation. A previously developed Markov transition probability model for solute transport is used in conjunction with a new conditional probability-based model of the sorption and desorption rates based on breakthrough curve data. Two models for prediction of spatially varying sorption and desorption rates along a one-dimensional streamline are developed. These models are a Markov model that utilizes conditional probabilities to determine the rates and an ensemble Kalman filter (EnKF) applied to the conditional probability method. Both approaches rely on a previously developed Markov-model of mass transfer, and both models assimilate the observed concentration data into the rate estimation at each observation time. Initial values of the rates are perturbed from the true values to form ensembles of rates and the ability of both estimation approaches to recover the true rates is examined over three different sets of perturbations. The models accurately estimate the rates when the mean of the perturbations are zero, the unbiased case. For the cases containing some bias, addition of the ensemble Kalman filter is shown to improve accuracy of the rate estimation by as much as an order of magnitude. Sandia is a multi program laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under Contract DE-AC04-94AL85000. This work was supported under the Sandia Laboratory Directed Research and Development program.
Strain Rate Tensor Estimation in Cine Cardiac MRI Based on Elastic Image Registration
NASA Astrophysics Data System (ADS)
Sánchez-Ferrero, Gonzalo Vegas; Vega, Antonio Tristán; Grande, Lucilio Cordero; de La Higuera, Pablo Casaseca; Fernández, Santiago Aja; Fernández, Marcos Martín; López, Carlos Alberola
In this work we propose an alternative method to estimate and visualize the Strain Rate Tensor (SRT) in Magnetic Resonance Images (MRI) when Phase Contrast MRI (PCMRI) and Tagged MRI (TMRI) are not available. This alternative is based on image processing techniques. Concretely, image registration algorithms are used to estimate the movement of the myocardium at each point. Additionally, a consistency checking method is presented to validate the accuracy of the estimates when no golden standard is available. Results prove that the consistency checking method provides an upper bound of the mean squared error of the estimate. Our experiments with real data show that the registration algorithm provides a useful deformation field to estimate the SRT fields. A classification between regional normal and dysfunctional contraction patterns, as compared with experts diagnosis, points out that the parameters extracted from the estimated SRT can represent these patterns. Additionally, a scheme for visualizing and analyzing the local behavior of the SRT field is presented.
Estimation of Respiratory Rates Using the Built-in Microphone of a Smartphone or Headset.
Nam, Yunyoung; Reyes, Bersain A; Chon, Ki H
2016-11-01
This paper proposes accurate respiratory rate estimation using nasal breath sound recordings from a smartphone. Specifically, the proposed method detects nasal airflow using a built-in smartphone microphone or a headset microphone placed underneath the nose. In addition, we also examined if tracheal breath sounds recorded by the built-in microphone of a smartphone placed on the paralaryngeal space can also be used to estimate different respiratory rates ranging from as low as 6 breaths/min to as high as 90 breaths/min. The true breathing rates were measured using inductance plethysmography bands placed around the chest and the abdomen of the subject. Inspiration and expiration were detected by averaging the power of nasal breath sounds. We investigated the suitability of using the smartphone-acquired breath sounds for respiratory rate estimation using two different spectral analyses of the sound envelope signals: The Welch periodogram and the autoregressive spectrum. To evaluate the performance of the proposed methods, data were collected from ten healthy subjects. For the breathing range studied (6-90 breaths/min), experimental results showed that our approach achieves an excellent performance accuracy for the nasal sound as the median errors were less than 1% for all breathing ranges. The tracheal sound, however, resulted in poor estimates of the respiratory rates using either spectral method. For both nasal and tracheal sounds, significant estimation outliers resulted for high breathing rates when subjects had nasal congestion, which often resulted in the doubling of the respiratory rates. Finally, we show that respiratory rates from the nasal sound can be accurately estimated even if a smartphone's microphone is as far as 30 cm away from the nose.
Hagen, S J; Hofrichter, J; Szabo, A; Eaton, W A
1996-01-01
How fast can a protein fold? The rate of polypeptide collapse to a compact state sets an upper limit to the rate of folding. Collapse may in turn be limited by the rate of intrachain diffusion. To address this question, we have determined the rate at which two regions of an unfolded protein are brought into contact by diffusion. Our nanosecond-resolved spectroscopy shows that under strongly denaturing conditions, regions of unfolded cytochrome separated by approximately 50 residues diffuse together in 35-40 microseconds. This result leads to an estimate of approximately (1 microsecond)-1 as the upper limit for the rate of protein folding. Images Fig. 1 PMID:8876184
A method for decoding the neurophysiological spike-response transform.
Stern, Estee; García-Crescioni, Keyla; Miller, Mark W; Peskin, Charles S; Brezina, Vladimir
2009-11-15
Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.
Statistical technique for analysing functional connectivity of multiple spike trains.
Masud, Mohammad Shahed; Borisyuk, Roman
2011-03-15
A new statistical technique, the Cox method, used for analysing functional connectivity of simultaneously recorded multiple spike trains is presented. This method is based on the theory of modulated renewal processes and it estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connectivity an "influence function" is identified. This function recognises the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. In comparison to existing techniques, the Cox method has the following advantages: it does not use bins (binless method); it is applicable to cases where the sample size is small; it is sufficiently sensitive such that it estimates weak influences; it supports the simultaneous analysis of multiple influences; it is able to identify a correct connectivity scheme in difficult cases of "common source" or "indirect" connectivity. The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections. The results suggest that this method is highly successful for analysing functional connectivity of simultaneously recorded multiple spike trains.
Estimation of Circadian Body Temperature Rhythm Based on Heart Rate in Healthy, Ambulatory Subjects.
Sim, Soo Young; Joo, Kwang Min; Kim, Han Byul; Jang, Seungjin; Kim, Beomoh; Hong, Seungbum; Kim, Sungwan; Park, Kwang Suk
2017-03-01
Core body temperature is a reliable marker for circadian rhythm. As characteristics of the circadian body temperature rhythm change during diverse health problems, such as sleep disorder and depression, body temperature monitoring is often used in clinical diagnosis and treatment. However, the use of current thermometers in circadian rhythm monitoring is impractical in daily life. As heart rate is a physiological signal relevant to thermoregulation, we investigated the feasibility of heart rate monitoring in estimating circadian body temperature rhythm. Various heart rate parameters and core body temperature were simultaneously acquired in 21 healthy, ambulatory subjects during their routine life. The performance of regression analysis and the extended Kalman filter on daily body temperature and circadian indicator (mesor, amplitude, and acrophase) estimation were evaluated. For daily body temperature estimation, mean R-R interval (RRI), mean heart rate (MHR), or normalized MHR provided a mean root mean square error of approximately 0.40 °C in both techniques. The mesor estimation regression analysis showed better performance than the extended Kalman filter. However, the extended Kalman filter, combined with RRI or MHR, provided better accuracy in terms of amplitude and acrophase estimation. We suggest that this noninvasive and convenient method for estimating the circadian body temperature rhythm could reduce discomfort during body temperature monitoring in daily life. This, in turn, could facilitate more clinical studies based on circadian body temperature rhythm.
Horowitz, A.J.
2003-01-01
In the absence of actual suspended sediment concentration (SSC) measurements, hydrologists have used sediment rating (sediment transport) curves to estimate (predict) SSCs for subsequent flux calculations. Various evaluations of the sediment rating-curve method were made using data from long-term, daily sediment-measuring sites within large (>1 000 000 km2), medium ( 1000 km2), and small (<1000 km2) river basins in the USA and Europe relative to the estimation of suspended sediment fluxes. The evaluations address such issues as the accuracy of flux estimations for various levels of temporal resolution as well as the impact of sampling frequency on the magnitude of flux estimation errors. The sediment rating-curve method tends to underpredict high, and overpredict low SSCs. As such, the range of errors associated with concomitant flux estimates for relatively short time-frames (e.g. daily, weekly) are likely to be substantially larger than those associated with longer time-frames (e.g. quarterly, annually) because the over- and underpredictions do not have sufficient time to balance each other. Hence, when error limits must be kept under ??20%, temporal resolution probably should be limited to quarterly or greater. The evaluations indicate that over periods of 20 or more years, errors of <1% can be achieved using a single sediment rating curve based on data spanning the entire period. However, somewhat better estimates for the entire period, and markedly better annual estimates within the period, can be obtained if individual annual sediment rating curves are used instead. Relatively accurate (errors estimation
Respiratory rate estimation from the built-in cameras of smartphones and tablets.
Nam, Yunyoung; Lee, Jinseok; Chon, Ki H
2014-04-01
This paper presents a method for respiratory rate estimation using the camera of a smartphone, an MP3 player or a tablet. The iPhone 4S, iPad 2, iPod 5, and Galaxy S3 were used to estimate respiratory rates from the pulse signal derived from a finger placed on the camera lens of these devices. Prior to estimation of respiratory rates, we systematically investigated the optimal signal quality of these 4 devices by dividing the video camera's resolution into 12 different pixel regions. We also investigated the optimal signal quality among the red, green and blue color bands for each of these 12 pixel regions for all four devices. It was found that the green color band provided the best signal quality for all 4 devices and that the left half VGA pixel region was found to be the best choice only for iPhone 4S. For the other three devices, smaller 50 × 50 pixel regions were found to provide better or equally good signal quality than the larger pixel regions. Using the green signal and the optimal pixel regions derived from the four devices, we then investigated the suitability of the smartphones, the iPod 5 and the tablet for respiratory rate estimation using three different computational methods: the autoregressive (AR) model, variable-frequency complex demodulation (VFCDM), and continuous wavelet transform (CWT) approaches. Specifically, these time-varying spectral techniques were used to identify the frequency and amplitude modulations as they contain respiratory rate information. To evaluate the performance of the three computational methods and the pixel regions for the optimal signal quality, data were collected from 10 healthy subjects. It was found that the VFCDM method provided good estimates of breathing rates that were in the normal range (12-24 breaths/min). Both CWT and VFCDM methods provided reasonably good estimates for breathing rates that were higher than 26 breaths/min but their accuracy degraded concomitantly with increased respiratory rates
Ultrasonic 3-D Vector Flow Method for Quantitative In Vivo Peak Velocity and Flow Rate Estimation.
Holbek, Simon; Ewertsen, Caroline; Bouzari, Hamed; Pihl, Michael Johannes; Hansen, Kristoffer Lindskov; Stuart, Matthias Bo; Thomsen, Carsten; Nielsen, Michael Bachmann; Jensen, Jorgen Arendt
2017-03-01
Current clinical ultrasound (US) systems are limited to show blood flow movement in either 1-D or 2-D. In this paper, a method for estimating 3-D vector velocities in a plane using the transverse oscillation method, a 32×32 element matrix array, and the experimental US scanner SARUS is presented. The aim of this paper is to estimate precise flow rates and peak velocities derived from 3-D vector flow estimates. The emission sequence provides 3-D vector flow estimates at up to 1.145 frames/s in a plane, and was used to estimate 3-D vector flow in a cross-sectional image plane. The method is validated in two phantom studies, where flow rates are measured in a flow-rig, providing a constant parabolic flow, and in a straight-vessel phantom ( ∅=8 mm) connected to a flow pump capable of generating time varying waveforms. Flow rates are estimated to be 82.1 ± 2.8 L/min in the flow-rig compared with the expected 79.8 L/min, and to 2.68 ± 0.04 mL/stroke in the pulsating environment compared with the expected 2.57 ± 0.08 mL/stroke. Flow rates estimated in the common carotid artery of a healthy volunteer are compared with magnetic resonance imaging (MRI) measured flow rates using a 1-D through-plane velocity sequence. Mean flow rates were 333 ± 31 mL/min for the presented method and 346 ± 2 mL/min for the MRI measurements.
Extinction rate estimates for plant populations in revisitation studies: Importance of detectability
Kery, M.
2004-01-01
Many researchers have obtained extinction-rate estimates for plant populations by comparing historical and current records of occurrence. A population that is no longer found is assumed to have gone extinct. Extinction can then be related to characteristics of these populations, such as habitat type, size, or species, to test ideas about what factors may affect extinction. Such studies neglect the fact that a population may be overlooked, however, which may bias estimates of extinction rates upward. In addition, if populations are unequally detectable across groups to be compared, such as habitat type or population size, comparisons become distorted to an unknown degree. To illustrate the problem, I simulated two data sets, assuming a constant extinction rate, in which populations occurred in different habitats or habitats of different size and these factors affected their detectability The conventional analysis implicitly assumed that detectability equalled 1 and used logistic regression to estimate extinction rates. It wrongly identified habitat and population size as factors affecting extinction risk. In contrast, with capture-recapture methods, unbiased estimates of extinction rates were recovered. I argue that capture-recapture methods should be considered more often in estimations of demographic parameters in plant populations and communities.
TR-BREATH: Time-Reversal Breathing Rate Estimation and Detection.
Chen, Chen; Han, Yi; Chen, Yan; Lai, Hung-Quoc; Zhang, Feng; Wang, Beibei; Liu, K J Ray
2017-04-28
In this paper, we introduce TR-BREATH, a timereversal (TR) based contact-free breathing monitoring system. It is capable of breathing detection and multi-person breathing rate estimation within a short period of time using off-the-shelf WiFi devices. The proposed system exploits the channel state information (CSI) to capture the miniature variations in the environment caused by breathing. To magnify the CSI variations, TRBREATH projects CSIs into the TR resonating strength (TRRS) feature space and analyzes the TRRS by the Root-MUSIC and affinity propagation algorithms. Extensive experiment results indoor demonstrate a perfect detection rate of breathing. With only 10 seconds of measurement, a mean accuracy of 99% can be obtained for single-person breathing rate estimation under the non-line-of-sight (NLOS) scenario. Furthermore, it achieves a mean accuracy of 98:65% in breathing rate estimation for a dozen people under the line-of-sight (LOS) scenario and a mean accuracy of 98:07% in breathing rate estimation of 9 people under the NLOS scenario, both with 63 seconds of measurement. Moreover, TR-BREATH can estimate the number of people with an error around 1. We also demonstrate that TR-BREATH is robust against packet loss and motions. With the prevailing of WiFi, TR-BREATH can be applied for in-home and real-time breathing monitoring.
Estimation of the in vivo recombination rate for a plant RNA virus.
Tromas, Nicolas; Zwart, Mark P; Poulain, Maïté; Elena, Santiago F
2014-03-01
Phylogenomic evidence suggested that recombination is an important evolutionary force for potyviruses, one of the larger families of plant RNA viruses. However, mixed-genotype potyvirus infections are marked by low levels of cellular coinfection, precluding template switching and recombination events between virus genotypes during genomic RNA replication. To reconcile these conflicting observations, we evaluated the in vivo recombination rate (rg) of Tobacco etch virus (TEV; genus Potyvirus, family Potyviridae) by coinfecting plants with pairs of genotypes marked with engineered restriction sites as neutral markers. The recombination rate was then estimated using two different approaches: (i) a classical approach that assumed recombination between marked genotypes can occur in the whole virus population, rendering an estimate of rg = 7.762 × 10(-8) recombination events per nucleotide site per generation, and (ii) an alternative method that assumed recombination between marked genotypes can occur only in coinfected cells, rendering a much higher estimate of rg = 3.427 × 10(-5) recombination events per nucleotide site per generation. This last estimate is similar to the TEV mutation rate, suggesting that recombination should be at least as important as point mutation in creating variability. Finally, we compared our mutation and recombination rate estimates to those reported for animal RNA viruses. Our analysis suggested that high recombination rates may be an unavoidable consequence of selection for fast replication at the cost of low fidelity.
A Bayesian framework to estimate diversification rates and their variation through time and space
2011-01-01
Background Patterns of species diversity are the result of speciation and extinction processes, and molecular phylogenetic data can provide valuable information to derive their variability through time and across clades. Bayesian Markov chain Monte Carlo methods offer a promising framework to incorporate phylogenetic uncertainty when estimating rates of diversification. Results We introduce a new approach to estimate diversification rates in a Bayesian framework over a distribution of trees under various constant and variable rate birth-death and pure-birth models, and test it on simulated phylogenies. Furthermore, speciation and extinction rates and their posterior credibility intervals can be estimated while accounting for non-random taxon sampling. The framework is particularly suitable for hypothesis testing using Bayes factors, as we demonstrate analyzing dated phylogenies of Chondrostoma (Cyprinidae) and Lupinus (Fabaceae). In addition, we develop a model that extends the rate estimation to a meta-analysis framework in which different data sets are combined in a single analysis to detect general temporal and spatial trends in diversification. Conclusions Our approach provides a flexible framework for the estimation of diversification parameters and hypothesis testing while simultaneously accounting for uncertainties in the divergence times and incomplete taxon sampling. PMID:22013891
Detecting Identity by Descent and Estimating Genotype Error Rates in Sequence Data
Browning, Brian L.; Browning, Sharon R.
2013-01-01
Existing methods for identity by descent (IBD) segment detection were designed for SNP array data, not sequence data. Sequence data have a much higher density of genetic variants and a different allele frequency distribution, and can have higher genotype error rates. Consequently, best practices for IBD detection in SNP array data do not necessarily carry over to sequence data. We present a method, IBDseq, for detecting IBD segments in sequence data and a method, SEQERR, for estimating genotype error rates at low-frequency variants by using detected IBD. The IBDseq method estimates probabilities of genotypes observed with error for each pair of individuals under IBD and non-IBD models. The ratio of estimated probabilities under the two models gives a LOD score for IBD. We evaluate several IBD detection methods that are fast enough for application to sequence data (IBDseq, Beagle Refined IBD, PLINK, and GERMLINE) under multiple parameter settings, and we show that IBDseq achieves high power and accuracy for IBD detection in sequence data. The SEQERR method estimates genotype error rates by comparing observed and expected rates of pairs of homozygote and heterozygote genotypes at low-frequency variants in IBD segments. We demonstrate the accuracy of SEQERR in simulated data, and we apply the method to estimate genotype error rates in sequence data from the UK10K and 1000 Genomes projects. PMID:24207118
NASA Astrophysics Data System (ADS)
Hanike, Yusrianti; Sadik, Kusman; Kurnia, Anang
2016-02-01
This research implemented unemployment rate in Indonesia that based on Poisson distribution. It would be estimated by modified the post-stratification and Small Area Estimation (SAE) model. Post-stratification was one of technique sampling that stratified after collected survey data. It's used when the survey data didn't serve for estimating the interest area. Interest area here was the education of unemployment which separated in seven category. The data was obtained by Labour Employment National survey (Sakernas) that's collected by company survey in Indonesia, BPS, Statistic Indonesia. This company served the national survey that gave too small sample for level district. Model of SAE was one of alternative to solved it. According the problem above, we combined this post-stratification sampling and SAE model. This research gave two main model of post-stratification sampling. Model I defined the category of education was the dummy variable and model II defined the category of education was the area random effect. Two model has problem wasn't complied by Poisson assumption. Using Poisson-Gamma model, model I has over dispersion problem was 1.23 solved to 0.91 chi square/df and model II has under dispersion problem was 0.35 solved to 0.94 chi square/df. Empirical Bayes was applied to estimate the proportion of every category education of unemployment. Using Bayesian Information Criteria (BIC), Model I has smaller mean square error (MSE) than model II.
NASA Technical Reports Server (NTRS)
Rizvi, Farheen
2013-01-01
A report describes a model that estimates the orientation of the backup reaction wheel using the reaction wheel spin rates telemetry from a spacecraft. Attitude control via the reaction wheel assembly (RWA) onboard a spacecraft uses three reaction wheels (one wheel per axis) and a backup to accommodate any wheel degradation throughout the course of the mission. The spacecraft dynamics prediction depends upon the correct knowledge of the reaction wheel orientations. Thus, it is vital to determine the actual orientation of the reaction wheels such that the correct spacecraft dynamics can be predicted. The conservation of angular momentum is used to estimate the orientation of the backup reaction wheel from the prime and backup reaction wheel spin rates data. The method is applied in estimating the orientation of the backup wheel onboard the Cassini spacecraft. The flight telemetry from the March 2011 prime and backup RWA swap activity on Cassini is used to obtain the best estimate for the backup reaction wheel orientation.
Minimax Rate-optimal Estimation of High-dimensional Covariance Matrices with Incomplete Data.
Cai, T Tony; Zhang, Anru
2016-09-01
Missing data occur frequently in a wide range of applications. In this paper, we consider estimation of high-dimensional covariance matrices in the presence of missing observations under a general missing completely at random model in the sense that the missingness is not dependent on the values of the data. Based on incomplete data, estimators for bandable and sparse covariance matrices are proposed and their theoretical and numerical properties are investigated. Minimax rates of convergence are established under the spectral norm loss and the proposed estimators are shown to be rate-optimal under mild regularity conditions. Simulation studies demonstrate that the estimators perform well numerically. The methods are also illustrated through an application to data from four ovarian cancer studies. The key technical tools developed in this paper are of independent interest and potentially useful for a range of related problems in high-dimensional statistical inference with missing data.
Analysis of the optimal sampling rate for state estimation in sensor networks with delays.
Martínez-Rey, Miguel; Espinosa, Felipe; Gardel, Alfredo
2017-03-27
When addressing the problem of state estimation in sensor networks, the effects of communications on estimator performance are often neglected. High accuracy requires a high sampling rate, but this leads to higher channel load and longer delays, which in turn worsens estimation performance. This paper studies the problem of determining the optimal sampling rate for state estimation in sensor networks from a theoretical perspective that takes into account traffic generation, a model of network behaviour and the effect of delays. Some theoretical results about Riccati and Lyapunov equations applied to sampled systems are derived, and a solution was obtained for the ideal case of perfect sensor information. This result is also interesting for non-ideal sensors, as in some cases it works as an upper bound of the optimisation solution.
Minimax Rate-optimal Estimation of High-dimensional Covariance Matrices with Incomplete Data*
Cai, T. Tony; Zhang, Anru
2016-01-01
Missing data occur frequently in a wide range of applications. In this paper, we consider estimation of high-dimensional covariance matrices in the presence of missing observations under a general missing completely at random model in the sense that the missingness is not dependent on the values of the data. Based on incomplete data, estimators for bandable and sparse covariance matrices are proposed and their theoretical and numerical properties are investigated. Minimax rates of convergence are established under the spectral norm loss and the proposed estimators are shown to be rate-optimal under mild regularity conditions. Simulation studies demonstrate that the estimators perform well numerically. The methods are also illustrated through an application to data from four ovarian cancer studies. The key technical tools developed in this paper are of independent interest and potentially useful for a range of related problems in high-dimensional statistical inference with missing data. PMID:27777471
Controlling chaos in balanced neural circuits with input spike trains
NASA Astrophysics Data System (ADS)
Engelken, Rainer; Wolf, Fred
The cerebral cortex can be seen as a system of neural circuits driving each other with spike trains. Here we study how the statistics of these spike trains affects chaos in balanced target circuits.Earlier studies of chaos in balanced neural circuits either used a fixed input [van Vreeswijk, Sompolinsky 1996, Monteforte, Wolf 2010] or white noise [Lajoie et al. 2014]. We study dynamical stability of balanced networks driven by input spike trains with variable statistics. The analytically obtained Jacobian enables us to calculate the complete Lyapunov spectrum. We solved the dynamics in event-based simulations and calculated Lyapunov spectra, entropy production rate and attractor dimension. We vary correlations, irregularity, coupling strength and spike rate of the input and action potential onset rapidness of recurrent neurons.We generally find a suppression of chaos by input spike trains. This is strengthened by bursty and correlated input spike trains and increased action potential onset rapidness. We find a link between response reliability and the Lyapunov spectrum. Our study extends findings in chaotic rate models [Molgedey et al. 1992] to spiking neuron models and opens a novel avenue to study the role of projections in shaping the dynamics of large neural circuits.
Use of nonlinear identification in robust attitude and attitude rate estimation for SAMPEX
NASA Technical Reports Server (NTRS)
Mook, D. Joseph; Depena, Juan; Trost, Kelly; Wen, Jung; Mcpartland, Michael
1995-01-01
A method is described for obtaining optimal attitude estimation/identification algorithms for spacecraft lacking attitude rate measurement devices (rate gyros), and then demonstrated using actual flight data from the Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX) spacecraft. SAMPEX does not have on-board rate sensing, and relies on sun sensors and a three-axis magnetometer for attitude determination. The absence of rate data normally reduces both the total amount of data available and the sampling density (in time) by a substantial fraction. In addition, attitude data is occasionally unavailable (for example, during sun occultation). As a result, the sensitivity of the estimates to model uncertainty and to measurement noise increases. In order to maintain accuracy in the attitude estimates, there is an increased need for accurate models of the rotational dynamics. The Minimum Model Error(MME)/Least Square Correlation(LSC) algorithm accurately identifies an improved model for SAMPEX to be used during periods of complete data loss or extreme noise. The model correction is determined by estimating only one orbit(the identification pass) just prior to the assumed data loss(the prediction pass). The MME estimator correctly predicted the states during the identification phase, but more importantly determines the necessary model correction trajectory, d(t). The LSC algorithm is then used to find this trajectory's functional form, H(x(t)). The results show significant improvement of the new corrected model's attitude estimates as compared to the original uncorrected model's estimates. The possible functional form of the correction term is limited at this point in the study to functions strictly of the estimated states. The results, however, strongly suggest that functions based on the relative position of the satellite may also be possible candidates for future consideration.
Use of nonlinear identification in robust attitude and attitude rate estimation for SAMPEX
NASA Technical Reports Server (NTRS)
Mook, D. Joseph; Depena, Juan; Trost, Kelly; Wen, Jung; Mcpartland, Michael
1995-01-01
A method is described for obtaining optimal attitude estimation/identification algorithms for spacecraft lacking attitude rate measurement devices (rate gyros), and then demonstrated using actual flight data from the Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX) spacecraft. SAMPEX does not have on-board rate sensing, and relies on sun sensors and a three-axis magnetometer for attitude determination. The absence of rate data normally reduces both the total amount of data available and the sampling density (in time) by a substantial fraction. In addition, attitude data is occasionally unavailable (for example, during sun occultation). As a result, the sensitivity of the estimates to model uncertainty and to measurement noise increases. In order to maintain accuracy in the attitude estimates, there is an increased need for accurate models of the rotational dynamics. The Minimum Model Error(MME)/Least Square Correlation(LSC) algorithm accurately identifies an improved model for SAMPEX to be used during periods of complete data loss or extreme noise. The model correction is determined by estimating only one orbit(the identification pass) just prior to the assumed data loss(the prediction pass). The MME estimator correctly predicted the states during the identification phase, but more importantly determines the necessary model correction trajectory, d(t). The LSC algorithm is then used to find this trajectory's functional form, H(x(t)). The results show significant improvement of the new corrected model's attitude estimates as compared to the original uncorrected model's estimates. The possible functional form of the correction term is limited at this point in the study to functions strictly of the estimated states. The results, however, strongly suggest that functions based on the relative position of the satellite may also be possible candidates for future consideration.
An Estimation of the Star Formation Rate in the Perseus Complex
NASA Astrophysics Data System (ADS)
Mercimek, Seyma
2016-07-01
The detailed study of all sources are carried on, by comparing the number of existing cores and YSOs from observations with the prediction from column density PDFs. With this investigation, we found a relation between starless cores and protostars that cores may be considered progenitors of the next generation of protostars, assuming the rate of star formation in the recent past is similar to the rate in the near future. These are also new results which have not been investigated pre