Comparison of methods for estimating motor unit firing rate time series from firing times

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 400 ms 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.     

A nonparametric mixture model for cure rate estimation

Nonparametric methods have attracted less attention than their parametric counterparts for cure rate analysis. In this paper, we study a general nonparametric mixture model. The proportional hazards assumption is employed in modeling the effect of covariates on the failure time of patients who are not cured. The EM algorithm, the marginal likelihood approach, and multiple imputations are employed to estimate parameters of interest in the model. This model extends models and improves estimation methods proposed by other researchers. It also extends Cox's proportional hazards regression model by allowing a proportion of event-free patients and investigating covariate effects on that proportion. The model and its estimation method are investigated by simulations. An application to breast cancer data, including comparisons with previous analyses using a parametric model and an existing nonparametric model by other researchers, confirms the conclusions from the parametric model but not those from the existing nonparametric model.     

Improving false discovery rate estimation

MOTIVATION: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR). However, rigorous control of the FDR at a preselected level is often impractical. Consequently, it has been suggested to use the q-value as an estimate of the proportion of false discoveries among a set of significant findings. However, such an interpretation of the q-value may be unwarranted considering that the q-value is based on an unstable estimator of the positive FDR (pFDR). Another method proposes estimating the FDR by modeling p-values as arising from a beta-uniform mixture (BUM) distribution. Unfortunately, the BUM approach is reliable only in settings where the assumed model accurately represents the actual distribution of p-values. METHODS: A method called the spacings LOESS histogram (SPLOSH) is proposed for estimating the conditional FDR (cFDR), the expected proportion of false positives conditioned on having k 'significant' findings. SPLOSH is designed to be more stable than the q-value and applicable in a wider variety of settings than BUM. RESULTS: In a simulation study and data analysis example, SPLOSH exhibits the desired characteristics relative to the q-value and BUM. AVAILABILITY: The Web site www.stjuderesearch.org/statistics/splosh.html has links to freely available S-plus code to implement the proposed procedure.     

Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown

Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, optimal linear estimation methods are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more challenging. As an alternative to model-based estimation, neural networks and other machine learning tools could help to develop good estimators in these involved scenarios. However, if no benchmark is available it is difficult to assess how well suited these tools are for different applications in population genetics.Here we investigate feedforward neural networks for the estimation of the mutation rate based on the site frequency spectrum and compare their performance with model-based estimators. For this we use the model-based estimators introduced by Fu, Futschik et al., and Watterson that minimize the variance or mean squared error for no and free recombination. We find that neural networks reproduce these estimators if provided with the appropriate features and training sets. Remarkably, using the model-based estimators to adjust the weights of the training data, only one hidden layer is necessary to obtain a single estimator that performs almost as well as model-based estimators for low and high recombination rates, and at the same time provides a superior estimation method for intermediate recombination rates. We apply the method to simulated data based on the human chromosome 2 recombination map, highlighting its robustness in a realistic setting where local recombination rates vary and/or are unknown.     

Upper-limit mutation rate estimation for a plant RNA virus

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.     

Cyclic GMP alters the firing rate and thermosensitivity of hypothalamic neurons

The rostral hypothalamus, especially the preoptic-anterior hypothalamus (POAH), contains temperature-sensitive and -insensitive neurons that form synaptic networks to control thermoregulatory responses. Previous studies suggest that the cyclic nucleotide cGMP is an important mediator in this neuronal network, since hypothalamic microinjections of cGMP analogs produce hypothermia in several species. In the present study, immunohistochemisty showed that rostral hypothalamic neurons contain cGMP, guanylate cyclase (necessary for cGMP synthesis), and CNG A2 (an important cyclic nucleotide-gated channel). Extracellular electrophysiological activity was recorded from different types of neurons in rat hypothalamic tissue slices. Each recorded neuron was classified according to its thermosensitivity as well as its firing rate response to 2-100 microM 8-bromo-cGMP (a membrane-permeable cGMP analog). cGMP has specific effects on different neurons in the rostral hypothalamus. In the POAH, the cGMP analog decreased the spontaneous firing rate in 45% of temperature-sensitive and -insensitive neurons, an effect that is likely due to cGMP-enhanced hyperpolarizing K(+) currents. This decreased POAH activity could attenuate thermoregulatory responses and produce hypothermia during exposures to cool or neutral ambient temperatures. Although 8-bromo-cGMP did not affect the thermosensitivity of most POAH neurons, it did increase the warm sensitivity of neurons in other hypothalamic regions located dorsal, lateral, and posterior to the POAH. This increased thermosensitivity may be due to pacemaker currents that are facilitated by cyclic nucleotides. If some of these non-POAH thermosensitive neurons promote heat loss or inhibit heat production, then their increased thermosensitivity could contribute to cGMP-induced decreases in body temperature.     

Measurement and nature of firing rate adaptation in turtle spinal neurons

There is sparse literature on the profile of action potential firing rate (spike-frequency) adaptation of vertebrate spinal motoneurons, with most of the work undertaken on cells of the adult cat and young rat. Here, we provide such information on adult turtle motoneurons and spinal ventral-horn interneurons. We compared adaptation in response to intracellular injection of 30-s, constant-current stimuli into high-threshold versus low-threshold motoneurons and spontaneously firing versus non-spontaneously-firing interneurons. The latter were shown to possess some adaptive properties that differed from those of motoneurons, including a delayed initial adaptation and more predominant reversal of adaptation attributable to plateau potentials. Issues were raised concerning the interpretation of changes in the action potentials afterhyperpolarization shape parameters throughout spike-frequency adaptation. No important differences were demonstrated in the adaptation of the two motoneuron and two interneuron groups. Each of these groups, however, was modeled by its own unique combination of action potential shape parameters for the simulation of its 30-s duration of spike-frequency adaptation. Also, for a small sample of the very highest-threshold versus lowest-threshold motoneurons, the former group had significantly more adaptation than the latter. This finding was like that shown previously for cat motoneurons supplying fast- versus slow twitch motor units.     

Modeling the variability of firing rate of retinal ganglion cells.

Impulse trains simulating the maintained discharges of retinal ganglion cells were generated by digital realizations of the integrate-and-fire model. If the mean rate were set by a "bias" level added to "noise," the variability of firing would be related to the mean firing rate as an inverse square root law; the maintained discharges of retinal ganglion cells deviate systematically from such a relationship. A more realistic relationship can be obtained if the integrate-and-fire mechanism is "leaky"; with this refinement, the integrate-and-fire model captures the essential features of the data. However, the model shows that the distribution of intervals is insensitive to that of the underlying variability. The leakage time constant, threshold, and distribution of the noise are confounded, rendering the model unspecifiable. Another aspect of variability is presented by the variance of responses to repeated discrete stimuli. The variance of response rate increases with the mean response amplitude; the nature of that relationship depends on the duration of the periods in which the response is sampled. These results have defied explanation. But if it is assumed that variability depends on mean rate in the way observed for maintained discharges, the variability of responses to abrupt changes in lighting can be predicted from the observed mean responses. The parameters that provide the best fits for the variability of responses also provide a reasonable fit to the variability of maintained discharges.     

Seasonal-like plasticity of spontaneous firing rate in a songbird pre-motor nucleus

Many animals exhibit seasonal changes in behavior and its underlying neural substrates. In seasonally breeding songbirds, the brain nuclei that control song learning and production undergo substantial structural changes at the onset of each breeding season, in association with changes in song behavior. These changes are largely mediated by photoperiod-dependent changes in circulating concentrations of gonadal steroid hormones. Little is known, however, about whether changes in the electrophysiological activity of neurons accompany the dramatic morphological changes in the song nuclei. Here we induced seasonal-like changes in the song systems of adult white-crowned sparrows and used extracellular recording in acute brain slices from those individuals to study physiological properties of neurons in the robust nucleus of the arcopallium (RA), a pre-motor nucleus necessary for song production. We report that: RA neurons from birds in breeding condition show a more than twofold increase in spontaneous firing rate compared to those from nonbreeding condition; this change appears to require both androgenic and estrogenic actions; and this change is intrinsic to the RA neurons. Thus, neurons in the song circuit exhibit both morphological and physiological adult seasonal plasticity.     

REVCOM: a robust Bayesian method for evolutionary rate estimation

MOTIVATION: Evolutionary conservation estimated from a multiple sequence alignment is a powerful indicator of the functional significance of a residue and helps to predict active sites, ligand binding sites, and protein interaction interfaces. Many algorithms that calculate conservation work well, provided an accurate and balanced alignment is used. However, such a strong dependence on the alignment makes the results highly variable. We attempted to improve the conservation prediction algorithm by making it more robust and less sensitive to (1) local alignment errors, (2) overrepresentation of sequences in some branches and (3) occasional presence of unrelated sequences. RESULTS: A novel method is presented for robust constrained Bayesian estimation of evolutionary rates that avoids overfitting independent rates and satisfies the above requirements. The method is evaluated and compared with an entropy-based conservation measure on a set of 1494 protein interfaces. We demonstrated that approximately 62% of the analyzed protein interfaces are more conserved than the remaining surface at the 5% significance level. A consistent method to incorporate alignment reliability is proposed and demonstrated to reduce arbitrary variation of calculated rates upon inclusion of distantly related or unrelated sequences into the alignment.     

Universal false discovery rate estimation methodology for genome-wide association studies

Genome-wide case-control association studies aim at identifying significant differential markers between sick and healthy populations. With the development of large-scale technologies allowing the genotyping of thousands of single nucleotide polymorphisms (SNPs) comes the multiple testing problem and the practical issue of selecting the most probable set of associated markers. Several False Discovery Rate (FDR) estimation methods have been developed and tuned mainly for differential gene expression studies. However they are based on hypotheses and designs that are not necessarily relevant in genetic association studies. In this article we present a universal methodology to estimate the FDR of genome-wide association results. It uses a single global probability value per SNP and is applicable in practice for any study design, using any statistic. We have benchmarked this algorithm on simulated data and shown that it outperforms previous methods in cases requiring non-parametric estimation. We exemplified the usefulness of the method by applying it to the analysis of experimental genotyping data of three Multiple Sclerosis case-control association studies.     

Nonlinear estimation of specific growth rate for aerobic fermentation processes

The specific growth rate of the biomass, a very important parameter of almost every fermentation process, cannot be measured directly or estimated from related variables, as the concentrations of biomass, substrates, or products, due to the lack of reliable and cheap sensors. In this article a stable adaptive estimator of the specific growth rate is designed for those aerobic processes where the measurement of the oxygen uptake rate is available on-line. This particular approach can be applied also for other reaction rates if the model of the process satisfies some very general assumptions, which make the dynamics of the measured reaction rate a nonlinear function only of two unknown parameters, the specific growth rate and its time derivative. With respect to a previous similar approach, the new estimator has one additional parameter and a different nonlinear structure. From the analysis of the dynamics of the estimation error, a tuning criterion is derived, by which the two different algorithms can be compared under similar conditions. Simulation results show a good performance of both estimators for various kind of processes and disturbances. (c) 1995 John Wiley & Sons, Inc.     

An indirect method for absorption rate estimation: Flurothyl-induced seizures

This paper develops a method to estimate a minimal amount of flurothyl necessary to induce the seizures (the seizure threshold). A simple mathematical model is proposed which permits one to determine the drug absorption rate from the amount which has been administered and from the measured latency to onset of seizure. Experimental animal (rats) were exposed to a continuous intake of flurothyl in two different situations: either being alone in the airtight chamber or sharing it in a pair. In the latter case, we assume that the two rats uniformly share the infused drug. Our calculations estimate that approximately 20 μl of flurothyl is necessary to induced twitches, whereas 25 μl of flurothyl is the dose required for the induction of clonic seizures. The model can be used to estimate the threshold amounts of any drug producing obvious behavioral changes irrespective of the route of administration.     

Validation of machine learning approach for direct mutation rate estimation

Mutations are the primary source of all genetic variation. Knowledge about their rates is critical for any evolutionary genetic analyses, but for a long time, that knowledge has remained elusive and indirectly inferred. In recent years, parent–offspring comparisons have yielded the first direct mutation rate estimates. The analyses are, however, challenging due to high rate of false positives and no consensus regarding standardized filtering of candidate de novo mutations. Here, we validate the application of a machine learning approach for such a task and estimate the mutation rate for the guppy (Poecilia reticulata), a model species in eco-evolutionary studies. We sequenced 4 parents and 20 offspring, followed by screening their genomes for de novo mutations. The initial large number of candidate de novo mutations was hard-filtered to remove false-positive results. These results were compared with mutation rate estimated with a supervised machine learning approach. Both approaches were followed by molecular validation of all candidate de novo mutations and yielded similar results. The ML method uniquely identified three mutations, but overall required more hands-on curation and had higher rates of false positives and false negatives. Both methods concordantly showed no difference in mutation rates between families. Estimated here the guppy mutation rate is among the lowest directly estimated mutation rates in vertebrates; however, previous research has also found low estimated rates in other teleost fishes. We discuss potential explanations for such a pattern, as well as future utility and limitations of machine learning approaches.     

Allometric estimation of metabolic rate from heart rate in penguins

Studies of the relationship between heart rate (f(H)) and rate of oxygen consumption (V(.) (O(2))), which are then used to predict field metabolic rate, frequently fail to incorporate body mass as a predictive variable. This is a potentially important omission in the study of animals whose body mass fluctuates substantially during their annual cycle. In an attempt further to improve estimates of field metabolic rate from f(H), we re-evaluated data on M(b), f(H) and V(.) (O(2)) from previous studies of macaroni penguins (Eudyptes chrysolophus) and king penguins (Aptenodytes patagonicus) and derived a new relationship to integrate these three quantities. This relationship is at least as accurate and precise as previously determined relationships. We applied this same principle to published data on 11 of the 20 recognised penguin taxa to derive a relationship to predict V(.) (O(2)) from f(H) and M(b) in penguins of any species. This result has interesting implications in terms of reducing the logistical burden in studies of field metabolic rate.     

Microarray image analysis: background estimation using quantile and morphological filters

Background

Identification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions.

Results

Here we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30) and YMR135C (gid8) yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c). The observed interaction was confirmed by tandem affinity purification (TAP tag), verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any) on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not included in genome-wide yeast TAP tagging projects.

Conclusion

PIPE analysis can predict yeast protein-protein interactions. Also, PIPE analysis can be used to study the internal architecture of yeast protein complexes. The data also suggests that a finite set of short polypeptide signals seem to be responsible for the majority of the yeast protein-protein interactions.