scholarly journals Inference of population genetic structure from temporal samples of DNA

2019 ◽  
Author(s):  
Olivier François ◽  
Séverine Liégeois ◽  
Benjamin Demaille ◽  
Flora Jay
Keyword(s):  
Allele Frequency ◽  
Random Mating ◽  
Principal Component ◽  
Estimation Algorithm ◽  
Frequency Drift ◽  
Fast Estimation ◽  
Data Representations ◽  
Brownian Process ◽  
Multiple Strains ◽  
Individual Scores

AbstractThe recent years have seen a growing number of studies investigating evolutionary questions using ancient DNA techniques and temporal samples of DNA. To address these questions, one of the most frequently-used algorithm is based on principal component analysis (PCA). When PCA is applied to temporal samples, the sample dates are, however, ignored during analysis, which could lead to some misinterpretations of the results. Here we introduce a new factor analysis (FA) method for which individual scores are corrected for the effect of allele frequency drift through time. Based on a diffusion approximation, our approach approximates allele frequency drift in a random mating population by a Brownian process. Exact solutions for estimates of corrected factors are obtained, and a fast estimation algorithm is presented. We compared data representations obtained from the FA method with PCA and with PC projections in simulations of divergence and admixture scenarios. Then we applied FA with correction for temporal drift to study the evolution of hepatitis C virus in a patient infected by multiple strains, and to describe the population structure of ancient European samples.

scholarly journals Inferring Linkage Disequilibrium Between a Polymorphic Marker Locus and a Trait Locus in Natural Populations

Genetics ◽  
2000 ◽  
Vol 156 (1) ◽  
pp. 457-467 ◽  
Author(s):  
Z W Luo ◽  
S H Tao ◽  
Z-B Zeng
Keyword(s):  
Linkage Disequilibrium ◽  
Allele Frequency ◽  
Random Mating ◽  
Natural Populations ◽  
Polymorphic Marker ◽  
Marker Locus ◽  
Model Parameters ◽  
Phenotypic Variance ◽  
Wide Range ◽  
Trait Locus

Abstract Three approaches are proposed in this study for detecting or estimating linkage disequilibrium between a polymorphic marker locus and a locus affecting quantitative genetic variation using the sample from random mating populations. It is shown that the disequilibrium over a wide range of circumstances may be detected with a power of 80% by using phenotypic records and marker genotypes of a few hundred individuals. Comparison of ANOVA and regression methods in this article to the transmission disequilibrium test (TDT) shows that, given the genetic variance explained by the trait locus, the power of TDT depends on the trait allele frequency, whereas the power of ANOVA and regression analyses is relatively independent from the allelic frequency. The TDT method is more powerful when the trait allele frequency is low, but much less powerful when it is high. The likelihood analysis provides reliable estimation of the model parameters when the QTL variance is at least 10% of the phenotypic variance and the sample size of a few hundred is used. Potential use of these estimates in mapping the trait locus is also discussed.

Fast estimation algorithm for living body radar

2014 ◽  
Author(s):  
Dai Sasakawa ◽  
Keita Konno ◽  
Naoki Honma ◽  
Kentaro Nishimori ◽  
Nobuyasu Takemura ◽  
...  
Keyword(s):  
Estimation Algorithm ◽  
Living Body ◽  
Fast Estimation

scholarly journals Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Wenjing Zhao ◽  
Yue Chi ◽  
Yatong Zhou ◽  
Cheng Zhang
Keyword(s):  
Principal Component Analysis ◽  
Standard Deviation ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
Principal Component ◽  
Estimation Algorithm ◽  
Component Analysis ◽  
Noise Estimation ◽  
Noise Standard Deviation ◽  
Estimation Algorithms

SGK (sequential generalization of K-means) dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA) noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1) The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2) The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3) Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.

scholarly journals A Novel Adaptive Frequency Estimation Algorithm Based on Interpolation FFT and Improved Adaptive Notch Filter

2017 ◽  
Vol 17 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Ting-ao Shen ◽  
Hua-nan Li ◽  
Qi-xin Zhang ◽  
Ming Li
Keyword(s):  
Convergence Rate ◽  
High Frequency ◽  
Frequency Estimation ◽  
Notch Filter ◽  
Estimation Algorithm ◽  
Signal Frequency ◽  
Tracking Accuracy ◽  
Frequency Tracking ◽  
Adaptive Notch Filter ◽  
Fast Estimation

Abstract The convergence rate and the continuous tracking precision are two main problems of the existing adaptive notch filter (ANF) for frequency tracking. To solve the problems, the frequency is detected by interpolation FFT at first, which aims to overcome the convergence rate of the ANF. Then, referring to the idea of negative feedback, an evaluation factor is designed to monitor the ANF parameters and realize continuously high frequency tracking accuracy. According to the principle, a novel adaptive frequency estimation algorithm based on interpolation FFT and improved ANF is put forward. Its basic idea, specific measures and implementation steps are described in detail. The proposed algorithm obtains a fast estimation of the signal frequency, higher accuracy and better universality qualities. Simulation results verified the superiority and validity of the proposed algorithm when compared with original algorithms.

Fast Estimation Algorithm for Calculation of Reflectance Map based on Wiener Estimation Technique

2005 ◽  
Vol 12 (1) ◽  
pp. 20-24 ◽  
Author(s):  
Koichi Takase ◽  
Norimichi Tsumura ◽  
Toshiya Nakaguchi ◽  
Yoichi Miyake
Keyword(s):  
Estimation Algorithm ◽  
Estimation Technique ◽  
Fast Estimation ◽  
Wiener Estimation ◽  
Reflectance Map

scholarly journals Multivariate analysis of five chicken breed in Indonesia based on microsatellite allele frequency

2021 ◽  
Vol 19 (1) ◽  
pp. 48
Author(s):  
Ferdy Saputra ◽  
Tike Sartika ◽  
Anneke Anggraeni ◽  
Andi Baso Lompengeng Ishak ◽  
Komarudin Komarudin ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Hierarchical Clustering ◽  
Correspondence Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Frequency Data ◽  
White Leghorn ◽  
Microsatellite Allele ◽  
Allele Frequency Data ◽  
Combination Of Methods

<p class="MDPI17abstract"><strong>Objective: </strong>This study tries to examine several multivariate methods in classifying genetic diversity using microsatellite allele frequency data.</p><p class="MDPI17abstract"><strong>Methods: </strong>This study used microsatellite allele frequency data from White Leghorn (n = 48), Kampung (n = 48), Pelung (n = 24), Sentul (n = 24), and Black Kedu (n = 25) from Indonesian Research Institute for Animal Production. Allele frequency data were analyzed by the Neighbor-Joining (NJ) method using the POPTREE2 program. The data was also analyzed by the Principal Component Analysis (PCA), Correspondence Analysis (CA), and Hierarchical Clustering on Principal Components (HCPC) methods using the factoextra and FactoMineR packages in the R 4.0.0 program.<strong></strong></p><p class="MDPI17abstract"><strong>Results: </strong>Correspondence Analysis (CA) found Sentul is more closer to Black Kedu. However, based on NJ, PCA, and HCPC showed Sentul is closer to Kampung. Based on the value of Dimension 1, Correspondence Analysis (80.7%) can explain greater variation than PCA (58.9%). However, CA method generated different results compared to NJ, PCA, and HCPC. NJ, PCA, and HCPC found four chicken clusters, namely cluster 1 (White Leghorn), cluster 2 (Pelung), cluster 3 (Black Kedu), and cluster 4 (Kampung and Sentul).<strong></strong></p><p class="MDPI17abstract"><strong>Conclusions: </strong>In conclusion, HCPC is a better multivariate method for analyzing allele frequency data than PCA and CA. HCPC can be used to analyze allele frequency data better than PCA, because HCPC is a combination of methods from hierarchical clustering and principal components.</p>

Dynamic Displacement Estimation from Acceleration Measurements Using a Wireless Smart Sensor

2013 ◽  
Vol 558 ◽  
pp. 227-234
Author(s):  
Jong Woong Park ◽  
Sung Han Sim ◽  
Hyung Jo Jung ◽  
Billie F. Spencer
Keyword(s):  
Large Scale ◽  
Low Frequency ◽  
Estimation Algorithm ◽  
Reference Points ◽  
Displacement Measurement ◽  
Frequency Drift ◽  
Smart Sensor ◽  
Civil Structures ◽  
Dynamic Displacement ◽  
Displacement Estimation

A displacement measurement provides useful information for structural health monitoring (SHM) as it is directly related to stiffness of the structure. Most existing methods of direct measurement such as the Laser Doppler Vibrometer (LDV) and the Liner Variable Differential Transformer (LVDT) are known to have accurate performance but have difficulties particularly in the use of large-scale civil structures as the methods rely on fixed reference points. Alternatively, indirect methods have been developed and widely used methods are Global Positioning System (GPS), vision-based displacement measurement system and displacement estimation from acceleration record. Among the indirect method, the use of accelerometer provides simple and economical in term of both hardware installation and operation. The major problem using acceleration based displacement estimation is low frequency drift caused by double integration. Recently, dynamic displacement estimation algorithm that addresses low-frequency drift problem has been developed. This study utilizes Wireless Smart Sensor (WSN) for estimating dynamic displacement from acceleration measurement in combination with the recently developed displacement estimation algorithm. Integrated into WSN that are low-cost, wireless, compatible with accelerometers, and capable of onboard computation, the displacement can be measured without limit of location on large-scale civil structures. Thus, this approach has the significant potential to impact many applications that require displacement measurements. With the displacement estimation algorithm embedded, the WSN performs in-network data processing to estimate displacements at each distributed sensor location wirelessly using only measured acceleration data. To experimentally validate the performance of displacement estimation using WSN for the use in structures with multiple-degree of freedom, the random vibration test is conducted on the three-story shear building model. The estimated displacement is compared with the reference displacements measured from the laser displacement sensor and the result shows good agreement.

scholarly journals Harmonic Frequency Estimation Algorithm based on ESPRIT and MSWF in Power System

2015 ◽  
Vol 9 (1) ◽  
pp. 518-523 ◽  
Author(s):  
Sun Xiangwen ◽  
Cao Zhe ◽  
Tian Wei
Keyword(s):  
Power System ◽  
Simulation Experiment ◽  
Frequency Estimation ◽  
Wiener Filter ◽  
Estimation Algorithm ◽  
Frequency Resolution ◽  
Harmonic Frequency ◽  
Fast Estimation ◽  
Multistage Wiener Filter ◽  
Esprit Algorithm

This paper points out the deficiency in the current harmonic frequency estimation algorithm in power system. In order to improve the accuracy of detection and reduce the computational complexity, the study combined the ESPRIT algorithm with multistage Wiener filter (MSWF) recurrence to achieve fast estimation of harmonic frequency. Theoretical analysis and simulation experiment show that the algorithm had relatively low requirement for the amount of data, and demonstrated good frequency resolution characteristics and anti-jamming capability, which made it ideally suitable for harmonic analysis in power system.

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