Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures

BACKGROUND: Longitudinal data often have multiple (repeated) measures recorded along a time trajectory. For example, the two cohorts from the Framingham Heart Study (GAW13 Problem 1) contain 21 and 5 repeated measures for hypertension phenotypes as well as epidemiological risk factors, respectively. Direct modelling of a large number of serially and biologically correlated traits in the context of linkage analysis can be prohibitively complex. Alternatively, we may consider using univariate transformation for linkage analysis of longitudinal repeated measures. RESULTS: We evaluated the utility of three conventional summary measures (mean, slope, and principal components) for genetic linkage analysis of longitudinal phenotypes by analyzing the chromosome 10 data of the Framingham Heart Study. Except for the temporal slope, all of the summary methods and the multivariate analysis identified the previously reported region, marker GATA64A09, for systolic blood pressure or high blood pressure. Further analysis revealed that this region may harbor gene(s) affecting human blood pressure at multiple stages of life. CONCLUSION: We conclude that mean and principal components are feasible alternatives for genetic linkage analysis of longitudinal phenotypes, but the slope might have a separate genetic basis from that of the original longitudinal phenotypes

Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches

BACKGROUND: Current statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques. The purpose of this study was to further investigate the utility and properties of a novel pattern recognition technique (step-wise discriminant analysis) using the chromosome 10 linkage data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression. RESULTS: The three step-wise approaches were compared in terms of statistical significance and gene localization. Step-wise discriminant linkage analysis approach performed best; next was step-wise logistic regression; and step-wise linear regression was the least efficient because it ignored the categorical nature of disease phenotypes. Nevertheless, all three methods successfully identified the previously reported chromosomal region linked to human hypertension, marker GATA64A09. We also explored the possibility of using the discriminant analysis to detect gene × gene and gene × environment interactions. There was evidence to suggest the existence of gene × environment interactions between markers GATA64A09 or GATA115E01 and hypertension treatment and gene × gene interactions between markers GATA64A09 and GATA115E01. Finally, we answered the theoretical question "Is a trichotomous phenotype more efficient than a binary?" Unlike logistic regression, discriminant sib-pair linkage analysis might have more power to detect linkage to a binary phenotype than a trichotomous one. CONCLUSION: We confirmed our previous speculation that step-wise discriminant analysis is useful for genetic mapping of complex diseases. This analysis also supported the possibility of the pattern recognition technique for investigating gene × gene or gene × environment interactions

A Combination of Dilated Self-Attention Capsule Networks and Bidirectional Long- and Short-Term Memory Networks for Vibration Signal Denoising

As scalar neurons of traditional neural networks promote dimension reduction caused by pooling, it is a difficult task to extract the high-dimensional spatial features and long-term correlation of pure signals from the noisy vibration signal. To address the above issues, a vibration signal denoising method based on the combination of a dilated self-attention capsule network and bidirectional long short memory network (DACapsNet–BiLSTM) is proposed to extract high-dimensional spatial features and learn long-term correlations between two adjacent time steps. An improved self-attention module with spatial feature extraction ability was constructed based on the random distribution of noise, which is embedded into the capsule network for the extracted spatial features and denoising. The dilated convolution is integrated into the improved capsule network to expand the receptive field to obtain the spatial features of the vibration signal. The output of the capsule network was used as the input of the bidirectional long-term and short-term memory network to obtain the timing characteristics of the vibration signal. Numerical experiments demonstrated that DACapsNet–BiLSTM performs better than other signal denoising methods, in terms of signal-to-noise ratio, mean square error, and mean absolute error metrics

A Combination of Dilated Self-Attention Capsule Networks and Bidirectional Long- and Short-Term Memory Networks for Vibration Signal Denoising

As scalar neurons of traditional neural networks promote dimension reduction caused by pooling, it is a difficult task to extract the high-dimensional spatial features and long-term correlation of pure signals from the noisy vibration signal. To address the above issues, a vibration signal denoising method based on the combination of a dilated self-attention capsule network and bidirectional long short memory network (DACapsNet–BiLSTM) is proposed to extract high-dimensional spatial features and learn long-term correlations between two adjacent time steps. An improved self-attention module with spatial feature extraction ability was constructed based on the random distribution of noise, which is embedded into the capsule network for the extracted spatial features and denoising. The dilated convolution is integrated into the improved capsule network to expand the receptive field to obtain the spatial features of the vibration signal. The output of the capsule network was used as the input of the bidirectional long-term and short-term memory network to obtain the timing characteristics of the vibration signal. Numerical experiments demonstrated that DACapsNet–BiLSTM performs better than other signal denoising methods, in terms of signal-to-noise ratio, mean square error, and mean absolute error metrics

Tracking Control of Uncertain Neural Network Systems with Preisach Hysteresis Inputs: A New Iteration-Based Adaptive Inversion Approach

To describe the hysteresis nonlinearities in smart actuators, numerous models have been presented in the literature, among which the Preisach operator is the most effective due to its capability to capture multi-loop or sophisticated hysteresis curves. When such an operator is coupled with uncertain nonlinear dynamics, especially in noncanonical form, it is a challenging problem to develop techniques to cancel out the hysteresis effects and, at the same time, achieve asymptotic tracking performance. To address this problem, in this paper, we investigate the problem of iterative inverse-based adaptive control for uncertain noncanonical nonlinear systems with unknown input Preisach hysteresis, and a new adaptive version of the closest-match algorithm is proposed to compensate for the Preisach hysteresis. With our scheme, the stability and convergence of the closed-loop system can be established. The effectiveness of the proposed control scheme is illustrated through simulation and experimental results

Summary statistics for assemblies.

<p>Summary statistics for assemblies.</p

Next-Generation Sequencing-Based Transcriptome Analysis of <i>Helicoverpa armigera</i> Larvae Immune-Primed with <i>Photorhabdus luminescens</i> TT01

<div><p>Although invertebrates are incapable of adaptive immunity, immunal reactions which are functionally similar to the adaptive immunity of vertebrates have been described in many studies of invertebrates including insects. The phenomenon was termed immune priming. In order to understand the molecular mechanism of immune priming, we employed Illumina/Solexa platform to investigate the transcriptional changes of the hemocytes and fat body of <i>Helicoverpa armigera</i> larvae immune-primed with the pathogenic bacteria <i>Photorhabdus luminescens</i> TT01. A total of 43.6 and 65.1 million clean reads with 4.4 and 6.5 gigabase sequence data were obtained from the TT01 (the immune-primed) and PBS (non-primed) cDNA libraries and assembled into 35,707 all-unigenes (non-redundant transcripts), which has a length varied from 201 to 16,947 bp and a N50 length of 1,997 bp. For 35,707 all-unigenes, 20,438 were functionally annotated and 2,494 were differentially expressed after immune priming. The differentially expressed genes (DEGs) are mainly related to immunity, detoxification, development and metabolism of the host insect. Analysis on the annotated immune related DEGs supported a hypothesis that we proposed previously: the immune priming phenomenon observed in <i>H. armigera</i> larvae was achieved by regulation of key innate immune elements. The transcriptome profiling data sets (especially the sequences of 1,022 unannotated DEGs) and the clues (such as those on immune-related signal and regulatory pathways) obtained from this study will facilitate immune-related novel gene discovery and provide valuable information for further exploring the molecular mechanism of immune priming of invertebrates. All these will increase our understanding of invertebrate immunity which may provide new approaches to control insect pests or prevent epidemic of infectious diseases in economic invertebrates in the future.</p></div

Length distribution of contig/unigene/all-unigene assembled by Trinity.

<p>X-axis represents sequence size (nt). Y-axis indicates the value of lg ^{sequence-numbers}.</p

COG function classification.

<p>The capital letters in x-axis indicates the COG categories as listed on the right of the histogram and the y-axis indicates the number of unigenes.</p