Random Drift Model Research Articles

Genome-wide analysis reveals the contributors to fast molecular evolution of the Chinese hook snout carp (Opsariichthys bidens)

Variations in molecular evolutionary rate have been widely investigated among lineages and genes. However, it remains an open question whether fast rate of molecular evolution is driven by natural selection or random drift, and how the fast rate is linked to metabolic rate. Additionally, previous studies on fast molecular evolution have been largely restricted to concatenated matrix of genes or a few specifically selected genes, but less is known for individual genes at the genome-wide level. Here we addressed these questions using more than 5000 single-copy orthologous (SCO) genes through comparative genomic and phylogenetic analyses among fishes, with a special focus on a newly-sequenced clupeocephalan fish the Chinese hook snout carp Opsariichthys bidens. We showed O. bidens displays significantly higher mean substitution rate and more fast-evolving SCO genes (2172 genes) than most fishes studied here. The rapidly evolving genes are enriched in highly conserved and very basic functions such as translation and ribosome that are critical for biological fitness. We further revealed that ∼25 % of these fast-evolving genes exhibit a constant increase of substitution rate from the common ancestor down to the present, suggesting a neglected but important contribution from ancestral states. Model fitting showed that ∼85 % of fast-evolving genes exclusive to O. bidens and related species follow the adaptive evolutionary model rather than random-drift model, and 7.6 % of fast-evolving genes identified in O. bidens have experienced positive selection, both indicating the reflection of adaptive selection. Finally, metabolic rate was observed to be linked with substitution rate in a gene-specific manner. Overall, our findings reveal fast molecular evolution of SCO genes at genome-wide level in O. bidens, and uncover the evolutionary and ecological contributors to it.

Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s2 to −1.2646 × 10−7 m/s2, while the variance decreases from 0.0133 m/s2 to 1.0975 × 10−5 m/s2. In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.

Adaptive [formula omitted] Kalman filter based random drift modeling and compensation method for ring laser gyroscope

This paper investigates the random drift influence on ring laser gyroscope (RLG) and proposes a random drift modeling and compensation method to improve the measurement accuracy of RLG. The traditional compensation method for random drift is the standard Kalman filter. However, it needs to know the accurate discrete-time state-space model and the statistic characteristics of process and measurement noises, which limits the performance of Kalman filter. Therefore, based on the autoregressive moving average (ARMA) model, this paper incorporates the H∞ technique and adaptive law design into the standard Kalman filter to deal with the system uncertainty and estimate the statistic characteristics of unknown noises. To test the performance of our proposed filter, the Allan variance method is adopted to analyze the random drift influence under different Kalman-type filters. The experimental results have been given to demonstrate that under the proposed adaptive H∞ Kalman filter, the influence of random drift on RLG output is largely reduced and the calibration accuracy for turntable is improved significantly.

Dominant Cubic Coefficients of the ‘1/3-Rule’ Reduce Contest Domains

Antagonistic exploitation in competition with a cooperative strategy defines a social dilemma, whereby eventually overall fitness of the population decreases. Frequency-dependent selection between two non-mutating strategies in a Moran model of random genetic drift yields an evolutionary rule of biological game theory. When a singleton fixation probability of co-operation exceeds the selectively neutral value being the reciprocal of population size, its relative frequency in the population equilibrates to less than 1/3. Maclaurin series of a singleton type fixation probability function calculated at third order enables the convergent domain of the payoff matrix to be identified. Asymptotically dominant third order coefficients of payoff matrix entries were derived. Quantitative analysis illustrates non-negligibility of the quadratic and cubic coefficients in Maclaurin series with selection being inversely proportional to population size. Novel corollaries identify the domain of payoff matrix entries that determines polarity of second order terms, with either non-harmful or harmful contests. Violation of this evolutionary rule observed with non-harmful contests depends on the normalized payoff matrix entries and selection differential. Significant violations of the evolutionary rule were not observed with harmful contests.

Random Drift Modeling and Compensation for MEMS-Based Gyroscopes and Its Application in Handwriting Trajectory Reconstruction

Microelectromechanical system (MEMS)-based gyroscopes have been widely applied to various inertial-sensing-based human–computer interaction (HCI) devices. However, the random drift of MEMS-based gyroscopes limits their applications. Hence, studies pay attention to develop various models to model and compensate the random drift for improving the performance of the MEMS-based gyroscopes. This paper presents a self-constructing Wiener-type recurrent neural network (SCWRNN) with its false nearest-neighbors-based self-constructing strategy and recursive recurrent learning algorithm to model the random drift of the MEMS-based gyroscopes and then compensate them from the calibrated gyroscope measurement. Subsequently, the proposed random drift modeling and compensation algorithm is integrated into the handwriting trajectory reconstruction algorithm of the inertial-sensing-based HCI device, called IMUPEN, for accurately obtaining the reconstructed handwriting trajectory. Users can hold the IMUPEN, which is composed of an accelerometer, two gyroscopes, a microcontroller, and an RF wireless transmission module, to write numerals at normal speed. The accelerations and angular velocities measured by the accelerometer and gyroscopes are transmitted to a personal computer through the RF module for further reconstructing the handwriting trajectory via the handwriting trajectory reconstruction algorithm. In addition, we have developed the SCWRNN-based random drift modeling and compensation algorithm to eliminate the cumulative errors caused by the random drift of the MEMS-based gyroscopes for further increasing the accuracy of handwriting trajectory reconstruction. Our experimental results have successfully validated the effectiveness of the proposed random drift modeling and compensation algorithm and its application in handwriting trajectory reconstruction.

Sleep in a comparative context: Investigating how human sleep differs from sleep in other primates

Primates vary in their sleep durations and, remarkably, humans sleep the least per 24-hr period of the 30 primates that have been studied. Using phylogenetic methods that quantitatively situate human phenotypes within a broader primate comparative context, we investigated the evolution of human sleep architecture, focusing on: total sleep duration, rapid eye movement (REM) sleep duration, non-rapid eye movement (NREM) sleep duration, and proportion of sleep in REM. We used two different Bayesian methods: phylogenetic prediction based on phylogenetic generalized least squares and a multistate Onrstein-Uhlenbeck (OU) evolutionary model of random drift and stabilizing selection. Phylogenetic prediction confirmed that humans sleep less than predicted for a primate of our body mass, predation risk, brain size, foraging needs, sexual selection, and diet. These analyses further revealed that humans pack an unexpectedly higher proportion of REM sleep within a shorter overall sleep duration, and do so by reducing NREM sleep (rather than increasing REM). The OU model generally confirmed these findings, with shifts along the human lineage inferred for TST, NREM, and proportion of REM, but not for REM. We propose that the risks and opportunity costs of sleep are responsible for shorter sleep durations in humans, with risks arising from terrestrial sleep involving threats from predators and conspecifics, and opportunity costs because time spent sleeping could be used for learning, creating material objects, and socializing.

Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization

MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization). Comparing the effect of modeling the MEMS gyroscope random drift with BP-ANN (back propagation artificial neural network) and the proposed method, the results showed that the latter had a better prediction accuracy. Using the compensation of three groups of MEMS gyroscope random drift data, the standard deviation of three groups of experimental data dropped from 0.00354, 0.00412, and 0.00328 to 0.00065, 0.00072 and 0.00061, respectively, which demonstrated that the proposed method can reduce the influence of MEMS gyroscope random drift and verified the effectiveness of this method for modeling MEMS gyroscope random drift.

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

A General Solution of the Wright–Fisher Model of Random Genetic Drift

We develop a general solution for the Fokker-Planck (Kolomogorov) equation representing the diffusion limit of the Wright-Fisher model of random genetic drift for an arbitrary number of alleles at a single locus. From this solution, we can readily deduce information about the evolution of a Wright-Fisher population.

Random genetic drift, natural selection, and noise in human cranial evolution.

This study assesses the extent to which relationships among groups complicate comparative studies of adaptation in recent human cranial variation and the extent to which departures from neutral additive models of evolution hinder the reconstruction of population relationships among groups using cranial morphology. Using a maximum likelihood evolutionary model fitting approach and a mixed population genomic and cranial data set, I evaluate the relative fits of several widely used models of human cranial evolution. Moreover, I compare the goodness of fit of models of cranial evolution constrained by genomic variation to test hypotheses about population specific departures from neutrality. Models from population genomics are much better fits to cranial variation than are traditional models from comparative human biology. There is not enough evolutionary information in the cranium to reconstruct much of recent human evolution but the influence of population history on cranial variation is strong enough to cause comparative studies of adaptation serious difficulties. Deviations from a model of random genetic drift along a tree-like population history show the importance of environmental effects, gene flow, and/or natural selection on human cranial variation. Moreover, there is a strong signal of the effect of natural selection or an environmental factor on a group of humans from Siberia. The evolution of the human cranium is complex and no one evolutionary process has prevailed at the expense of all others. A holistic unification of phenome, genome, and environmental context, gives us a strong point of purchase on these problems, which is unavailable to any one traditional approach alone. Am J Phys Anthropol 160:582-592, 2016. © 2016 Wiley Periodicals, Inc.

Not guaranteeing convergence of differential evolution on a class of multimodal functions

The theoretical studies of differential evolution algorithm (DE) have gradually attracted the attention of more and more researchers. According to recent researches, the classical DE cannot guarantee global convergence in probability except for some special functions. Along this perspective, a problem aroused is that on which functions DE cannot guarantee global convergence. This paper firstly addresses that DE variants are difficult on solving a class of multimodal functions (such as the Shifted Rotated Ackley's function) identified by two characteristics. One is that the global optimum of the function is near a boundary of the search space. The other is that the function has a larger deceptive optima set in the search space. By simplifying the class of multimodal functions, this paper then constructs a Linear Deceptive function. Finally, this paper develops a random drift model of the classical DE algorithm to prove that the algorithm cannot guarantee global convergence on the class of functions identified by the two above characteristics.

Research on Signal Processing of MEMS Gyro Array

A new random drift model and the measured angular rate model of MEMS gyro are presented. Based on such models, signal processing techniques are used to decrease gyro drift. Kalman filtering equations have been built for static measurement and dynamic measurement of the gyro array, which combinesNindividual gyros into a single rate estimate. By selecting the favorable cross correlation coefficient between individual gyros in the noise correlation matrix, the gyro array performance can be significantly improved over that of any individual component device. A new gyro array dynamic measurement procession is also presented. Data fusion of the difference between individual gyro dynamic measurements can identify every gyro real-time drift out and get its noisy test. Based on the laws of the gyro curve motion, the tested dynamic signal is filtered to improve the gyro accuracy. All these processings have been implemented by digital signal processor. Simulation results show that the static drift can decrease from 22.1°/h to 0.184°/h and the dynamic drift can decrease from 22.1°/h to 8.98°/h.

Modeling and Optimizing of Random Gyro Drift Based on AR and GP

Gyroscope is the key component in an Inertial Navigation System (INS). It depends on the precision of the INS. The nonlinear random drift error model based on autoregressive (AR) and genetic programming (GP) was established. The linear model is established based on AR technique. After that, the nonlinear model is built based on GP technique. The result indicates that the square error of the random gyro drift is reduced by 74.5%. The hybrid modeling method can effectively compensate the random gyro drift and improve the stability of the system.

Similar patterns of clonally expanded somatic mtDNA mutations in the colon of heterozygous mtDNA mutator mice and ageing humans

Clonally expanded mitochondrial DNA (mtDNA) mutations resulting in focal respiratory chain deficiency in individual cells are proposed to contribute to the ageing of human tissues that depend on adult stem cells for self-renewal; however, the consequences of these mutations remain unclear. A good animal model is required to investigate this further; but it is unknown whether mechanisms for clonal expansion of mtDNA mutations, and the mutational spectra, are similar between species. Here we show that mice, heterozygous for a mutation disrupting the proof-reading activity of mtDNA polymerase (PolgA+/mut) resulting in an increased mtDNA mutation rate, accumulate clonally expanded mtDNA point mutations in their colonic crypts with age. This results in focal respiratory chain deficiency, and by 81 weeks of age these animals exhibit a similar level and pattern of respiratory chain deficiency to 70-year-old human subjects. Furthermore, like in humans, the mtDNA mutation spectrum appears random and there is an absence of selective constraints. Computer simulations show that a random genetic drift model of mtDNA clonal expansion can accurately model the data from the colonic crypts of wild-type, PolgA+/mut animals, and humans, providing evidence for a similar mechanism for clonal expansion of mtDNA point mutations between these mice and humans.

ARMA-AKF Model of MEMS Gyro Rotation Data Random Drift Compensation

Application of MEMS angular rate gyro attitude when monitoring for long-term, zero-point drift and random error in cumulative points after long-term monitoring errors can significantly increase the measurement error. Using ARMA model of MEMS gyros random drift modeling and error compensation method using Adaptive Kalman Filter, which increases gyro attitude measurement of long-term reliability Last experiment. The method in accordance with the principle of time series analysis, integration of observations and estimates of actual system so that it can reflect the influence of external disturbance and noise on the system. It can also reflect the influence of system disturbance on actual system performance improves estimation precision.

MEMS Gyro Random Drift Model Parameter Identification Based on Two-Stage Recursive Least Squares Method

Autoregressive moving average model (ARMA) was usually used for gyro random drift modeling. Because gyro random drift was a non-stationary, weak non-linear and time-variant random signal, model parameters were random and time-variant, too. For improving precision of gyro and reducing effects of random drift, this paper adopted two-stage recursive least squares method for ARMA parameter estimation. This method overcame the shortcomings of the conventional recursive extended least squares (RELS) algorithm. At the same time, the forgetting factor was introduced to adapt the model parameters change. The simulation experimental results showed that this method is effective.

Research on Random Drift Modeling and Error Compensation Method for MEMS Gyroscope

This paper presents modeling random drift by traditional time series method. In the modeling process, the real time average algorithm is proposed which can extract the constant drift of the MEMS gyroscope effectively to get random drift, and a modified recursive extended least squares (RELS) method for parameter estimation of the autoregressive moving average models (ARMA) is presented. The modified RELS algorithm consists of two-stage RLS algorithm which can on line be implemented, and has the fast convergence rate. After modeling, the corresponding Kalman filter is designed to make compensation for gyroscope random drift. The compensation results for the practical testing data of the MEMS gyroscope show that the model established can reflect the gyroscope random drift tendency with reasonable accuracy, and the gyroscope random drift can be effectively reduced by the filtering method presented.

Comparison and content of the Wright–Fisher model of random genetic drift, the diffusion approximation, and an intermediate model

We investigate the detailed connection between the Wright–Fisher model of random genetic drift and the diffusion approximation, under the assumption that selection and drift are weak and so cause small changes over a single generation. A representation of the mathematics underlying the Wright–Fisher model is introduced which allows the connection to be made with the corresponding mathematics underlying the diffusion approximation. Two ‘hybrid’ models are also introduced which lie ‘between’ the Wright–Fisher model and the diffusion approximation. In model 1 the relative allele frequency takes discrete values while time is continuous; in model 2 time is discrete and relative allele frequency is continuous. While both hybrid models appear to have a similar status and the same level of plausibility, the different nature of time and frequency in the two models leads to significant mathematical differences. Model 2 is mathematically inconsistent and has to be ruled out as being meaningful. Model 1 is used to clarify the content of Kimura's solution of the diffusion equation, which is shown to have the natural interpretation as describing only those populations where alleles are segregating. By contrast the Wright–Fisher model and the solution of the diffusion equation of McKane and Waxman cover populations of all categories, namely populations where alleles segregate, are lost, or fix.

Prevalence, segregation, and phenotype of the mitochondrial DNA 3243A>G mutation in children

We studied the prevalence, segregation, and phenotype of the mitochondrial DNA 3243A>G mutation in children in a defined population in Northern Ostrobothnia, Finland. Children with diagnoses commonly associated with mitochondrial diseases were ascertained. Blood DNA from 522 selected children was analyzed for 3243A>G. Children with the mutation were clinically examined. Information on health history before the age of 18 years was collected from previously identified adult patients with 3243A>G. Mutation segregation analysis in buccal epithelial cells was performed in mothers with 3243A>G and their children whose samples were analyzed anonymously. Eighteen children were found to harbor 3243A>G in a population of 97,609. A minimum estimate for the prevalence of 3243A>G was 18.4 in 100,000 (95% confidence interval, 10.9-29.1/100,000). Information on health in childhood was obtained from 37 adult patients with 3243A>G. The first clinical manifestations appearing in childhood were sensorineural hearing impairment, short stature or delayed maturation, migraine, learning difficulties, and exercise intolerance. Mutation analysis from 13 mothers with 3243A>G and their 41 children gave a segregation rate of 0.80. The mothers with heteroplasmy greater than 50% tended to have offspring with lower or equal heteroplasmy, whereas the opposite was true for mothers with heteroplasmy less than or equal to 50% (p = 0.0016). The prevalence of 3243A>G is relatively high in the pediatric population, but the morbidity in children is relatively low. The random genetic drift model may be inappropriate for the transmission of the 3243A>G mutation.

Biogeographic Evidence for Selection on Mitochondrial DNA in North Pacific Walleye Pollock Theragra chalcogramma

Three major mitochondrial DNA (mtDNA) haplogroups were identified in 5 data sets for North Pacific and Bering Sea walleye pollock. The common haplogroup A showed mirror-image clines on both sides of the North Pacific with high frequencies in southern areas (P(A) > 0.84) and low frequencies in the Bering Sea (P(A) < 0.36). Two additional haplogroups showed complimentary, but weaker, clines in the opposite direction. These clines are unlikely to have arisen by chance during postglacial colonizations of coastal waters in the North Pacific and Bering Sea, and they do not appear to reflect isolation by distance. Contrary to these trends, pollock at the western end of the Aleutian Island Archipelago were genetically more similar to Asian than to North American pollock, a pattern likely reflecting postglacial colonization. Haplogroup F(ST) values for a given haplotype diversity were significantly larger than expected under the island model of migration and random drift, a result implicating natural selection. Frequencies of haplogroup A were highly correlated with sea surface temperature (r > 0.91), whereas frequencies of groups B and C showed negative correlations with temperature. Selection may be operating directly on mtDNA variability or may be mediated through cytonuclear interactions. This biogeographic evidence adds to a growing body of literature indicating that selection may play a greater role in sculpting mtDNA variability than previously thought.