scholarly journals Bayesian multi-trait kernel methods improve multi-environment genome based prediction

2021 ◽  
Author(s):  
Osval Antonio Montesinos-López ◽  
José Cricelio Montesinos-López ◽  
Abelardo Montesinos-Lopez ◽  
Juan Manuel Ramírez-Alcaraz ◽  
Jesse Poland ◽  
...  
Keyword(s):  
Kernel Methods ◽  
Genomic Prediction ◽  
Linear Models ◽  
Kernel Method ◽  
Prediction Performance ◽  
Gaussian Kernel ◽  
Phenotypic Traits ◽  
Proposed Model ◽  
The Mean ◽  
Gaussian Polynomial

Abstract When multi-trait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this paper we explore Bayesian multi-trait kernel methods for genomic prediction and we illustrate the power of these models with three real datasets. The kernels under study were the linear, Gaussian, polynomial and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multi-trait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multi-trait linear models by 2.2 to 17.45% (datasets 1 to 3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multi-trait kernel method can be attributed to the fact that the proposed model is able to capture non-linear patterns more efficiently than linear multi-trait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel.

scholarly journals Comparison of correlation analysis techniques for irregularly sampled time series

2011 ◽  
Vol 18 (3) ◽  
pp. 389-404 ◽  
Author(s):  
K. Rehfeld ◽  
N. Marwan ◽  
J. Heitzig ◽  
J. Kurths
Keyword(s):  
Time Series ◽  
Kernel Methods ◽  
Large Scale ◽  
Cross Correlation ◽  
Kernel Method ◽  
Linear Interpolation ◽  
Irregular Sampling ◽  
Gaussian Kernel ◽  
Robust Estimator ◽  
Mean Square Errors

Abstract. Geoscientific measurements often provide time series with irregular time sampling, requiring either data reconstruction (interpolation) or sophisticated methods to handle irregular sampling. We compare the linear interpolation technique and different approaches for analyzing the correlation functions and persistence of irregularly sampled time series, as Lomb-Scargle Fourier transformation and kernel-based methods. In a thorough benchmark test we investigate the performance of these techniques. All methods have comparable root mean square errors (RMSEs) for low skewness of the inter-observation time distribution. For high skewness, very irregular data, interpolation bias and RMSE increase strongly. We find a 40 % lower RMSE for the lag-1 autocorrelation function (ACF) for the Gaussian kernel method vs. the linear interpolation scheme,in the analysis of highly irregular time series. For the cross correlation function (CCF) the RMSE is then lower by 60 %. The application of the Lomb-Scargle technique gave results comparable to the kernel methods for the univariate, but poorer results in the bivariate case. Especially the high-frequency components of the signal, where classical methods show a strong bias in ACF and CCF magnitude, are preserved when using the kernel methods. We illustrate the performances of interpolation vs. Gaussian kernel method by applying both to paleo-data from four locations, reflecting late Holocene Asian monsoon variability as derived from speleothem δ18O measurements. Cross correlation results are similar for both methods, which we attribute to the long time scales of the common variability. The persistence time (memory) is strongly overestimated when using the standard, interpolation-based, approach. Hence, the Gaussian kernel is a reliable and more robust estimator with significant advantages compared to other techniques and suitable for large scale application to paleo-data.

Simple explicit model of liquid mean flow in region above axial impeller

1985 ◽  
Vol 50 (11) ◽  
pp. 2396-2410
Author(s):  
Miloslav Hošťálek ◽  
Ivan Fořt
Keyword(s):  
Vortex Flow ◽  
Flow Model ◽  
Quantitative Agreement ◽  
Mean Flow ◽  
Flat Bottom ◽  
Proposed Model ◽  
Minimum Number ◽  
The Mean ◽  
Model Equations ◽  
Radial Circulation

The study describes a method of modelling axial-radial circulation in a tank with an axial impeller and radial baffles. The proposed model is based on the analytical solution of the equation for vortex transport in the mean flow of turbulent liquid. The obtained vortex flow model is tested by the results of experiments carried out in a tank of diameter 1 m and with the bottom in the shape of truncated cone as well as by the data published for the vessel of diameter 0.29 m with flat bottom. Though the model equations are expressed in a simple form, good qualitative and even quantitative agreement of the model with reality is stated. Apart from its simplicity, the model has other advantages: minimum number of experimental data necessary for the completion of boundary conditions and integral nature of these data.

scholarly journals Modeling the Excess Velocity of Low-Viscous Taylor Droplets in Square Microchannels

Fluids ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 162 ◽  
Author(s):  
Thorben Helmers ◽  
Philip Kemper ◽  
Jorg Thöming ◽  
Ulrich Mießner
Keyword(s):  
High Speed ◽  
Process Intensification ◽  
Continuous Phase ◽  
Channel Cross Section ◽  
Optimization Approach ◽  
Mean Flow ◽  
Bypass Flow ◽  
Wall Film ◽  
Proposed Model ◽  
The Mean

Microscopic multiphase flows have gained broad interest due to their capability to transfer processes into new operational windows and achieving significant process intensification. However, the hydrodynamic behavior of Taylor droplets is not yet entirely understood. In this work, we introduce a model to determine the excess velocity of Taylor droplets in square microchannels. This velocity difference between the droplet and the total superficial velocity of the flow has a direct influence on the droplet residence time and is linked to the pressure drop. Since the droplet does not occupy the entire channel cross-section, it enables the continuous phase to bypass the droplet through the corners. A consideration of the continuity equation generally relates the excess velocity to the mean flow velocity. We base the quantification of the bypass flow on a correlation for the droplet cap deformation from its static shape. The cap deformation reveals the forces of the flowing liquids exerted onto the interface and allows estimating the local driving pressure gradient for the bypass flow. The characterizing parameters are identified as the bypass length, the wall film thickness, the viscosity ratio between both phases and the C a number. The proposed model is adapted with a stochastic, metaheuristic optimization approach based on genetic algorithms. In addition, our model was successfully verified with high-speed camera measurements and published empirical data.

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

Study on Association between Height, Weight, Iodine Supplementation and Thyroid Volume

2021 ◽  
pp. 1-25
Author(s):  
Lanchun Liu ◽  
Lixiang Liu ◽  
Ming Li ◽  
Yang Du ◽  
Peng Liu ◽  
...  
Keyword(s):  
Iodine Deficiency ◽  
Linear Models ◽  
Thyroid Volume ◽  
Salt Iodization ◽  
Iodine Supplementation ◽  
Random Pairing ◽  
Level Data ◽  
The Mean ◽  
Salt Iodine ◽  
The Difference

Abstract The policy of Universal Salt Iodization (USI) could reduce population’s thyroid volume (TVOL) in iodine deficiency areas. Conversely, the improved growth and developmental status of children might increase the TVOL accordingly. Whether the decreased TVOL by USI conceals the increase effect of height and weight on TVOL is unclear. The aim of this study was to analyse the association between height, weight, iodine supplementation and TVOL. Five national Iodine Deficiency Disorder surveys were matched into four pairs according to the purpose of analysis. County-level data of both detected by paired surveys were incorporated, 1: 1 random pairing method was used to match counties or individuals. The difference of TVOL between different height, weight, different iodine supplementation measures groups and the association between TVOL and them were studied. The mean height and weight of children aged 8-10 years increased from 129.9cm and 26.9kg in 2002 to 136.2cm and 32.1kg in 2019; while the median TVOL decreased from 3.10ml to 2.61ml. Iodine supplementation measures can affect TVOL; after exclude iodine effects, the median TVOL was increased with the height and weight. On the other side, after excluding the influence of height and weight, the median TVOL remained decreased. Only age, weight and salt iodine were significant associated with TVOL in multiple linear models. Development of height and weight in children is the evidence of improved nutrition. The decreased TVOL caused by iodized salt measures conceals the increase effect of height and weight on TVOL. Age, weight, and salt iodine affect TVOL significantly.

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

OBESITY AND SELF-REPORTED OUTCOME AFTER MINIMALLY INVASIVE LUMBAR SPINAL FUSION SURGERY

Neurosurgery ◽  
2008 ◽  
Vol 63 (5) ◽  
pp. 956-960 ◽  
Author(s):  
David S. Rosen ◽  
Sherise D. Ferguson ◽  
Alfred T. Ogden ◽  
Dezheng Huo ◽  
Richard G. Fessler
Keyword(s):  
Minimally Invasive ◽  
Linear Models ◽  
Short Form ◽  
Length Of Hospital Stay ◽  
Fusion Surgery ◽  
Body Habitus ◽  
Short Form 36 ◽  
Spinal Fusion Surgery ◽  
The Mean ◽  
Lumbar Spinal

Abstract OBJECTIVE Many patients undergoing lumbar spine fusion are overweight or obese. The relationship between body habitus and outcome after lumbar spine fusion surgery is not well defined. METHODS We analyzed a prospectively maintained database of self-reported pain and quality of life measures, including Visual Analog Scale pain score, Short Form 36, and Oswestry Disability Index. We selected patients undergoing minimally invasive transforaminal lumbar interbody fusion between September 2002 and June 2006 at a single institution. We used linear regression models and mixed-effects linear models to examine the relationships between body habitus and self-reported outcomes. RESULTS The analysis identified 110 patients meeting the study criteria, with a median follow-up period of 14.8 months. The mean age was 56 years, mean height was 169 cm, and mean weight was 82.2 kg. The mean body mass index (BMI) was 28.7 kg/m2; 31% of patients were overweight (BMI, 25–29.9), and 32% of patients were obese (BMI, >30). Linear regression analysis did not identify a correlation between weight or BMI and pre- and postsurgery changes in any of the outcome measures. The significant findings observed in the mixed-effects linear models were that the changing patterns of Short Form 36 Body Pain subscale and Short Form 36 Vitality subscale varied significantly by category of BMI (P = 0.01 and P = 0.002, respectively), but not significantly if continuous BMI was used (P = 0.53 and P = 0.46, respectively). BMI correlated marginally with estimated blood loss (P = 0.08), but not operative time, length of hospital stay, or complications. CONCLUSION Among this cohort of minimally invasive lumbar fusion patients, body habitus measured by BMI, weight, or height did not have a significant relationship with most self-reported outcome measures, operative time, length of hospital stay, or complications. Obesity should not be considered a contraindication to minimally invasive lumbar spinal fusion surgery.

scholarly journals Recommending Mobile Microblog Users via a Tensor Factorization Based on User Cluster Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangwen Liao ◽  
Lingying Zhang ◽  
Jingjing Wei ◽  
Dingda Yang ◽  
Guolong Chen
Keyword(s):  
Network Centrality ◽  
Network Clustering ◽  
Tensor Factorization ◽  
Latent Factors ◽  
Cp Decomposition ◽  
Temporal Features ◽  
User Influence ◽  
Proposed Model ◽  
The Mean ◽  
Content Similarity

User influence is a very important factor for microblog user recommendation in mobile social network. However, most existing user influence analysis works ignore user’s temporal features and fail to filter the marketing users with low influence, which limits the performance of recommendation methods. In this paper, a Tensor Factorization based User Cluster (TFUC) model is proposed. We firstly identify latent influential users by neural network clustering. Then, we construct a features tensor according to latent influential user’s opinion, activity, and network centrality information. Furthermore, user influences are predicted by the latent factors resulting from the temporal restrained CP decomposition. Finally, we recommend microblog users considering both user influence and content similarity. Our experimental results show that the proposed model significantly improves recommendation performance. Meanwhile, the mean average precision of TFUC outperforms the baselines with 3.4% at least.

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