Effects of slow and regular breathing exercise on cardiopulmonary coupling and blood pressure

Investigation of the interaction between cardiovascular variables and respiration provides a quantitative and noninvasive approach to assess the autonomic control of cardiovascular function. The aim of this paper is to investigate the changes of cardiopulmonary coupling (CPC), blood pressure (BP) and pulse transit time (PTT) during a stepwise-paced breathing (SPB) procedure (spontaneous breathing followed by paced breathing at 14, 12.5, 11, 9.5, 8 and 7 breaths per minute, 3 min each) and gain insights into the characteristics of slow breathing exercises. RR interval, respiration, BP and PTT are collected during the SPB procedure (48 healthy subjects, 27 ± 6 years). CPC is assessed through investigating both the phase and amplitude dynamics between the respiration-induced components from RR interval and respiration by the approach of ensemble empirical mode decomposition. It was found that even though the phase synchronization and amplitude oscillation of CPC were both enhanced by the SPB procedure, phase coupling does not increase monotonically along with the amplitude oscillation during the whole procedure. Meanwhile, BP was reduced significantly by the SPB procedure (SBP: from 122.0 ± 13.4 to 114.2 ± 14.9 mmHg, p < 0.001, DBP: from 82.2 ± 8.6 to 77.0 ± 9.8 mmHg, p < 0.001, PTT: from 172.8 ± 20.1 to 176.8 ± 19.2 ms, p < 0.001). Our results demonstrate that the SPB procedure can reduce BP and lengthen PTT significantly. Compared with amplitude dynamics, phase dynamics is a different marker for CPC analysis in reflecting cardiorespiratory coherence during slow breathing exercise. Our study provides a methodology to practice slow breathing exercise, including the setting of target breathing rate, change of CPC and the importance of regular breathing. The applications and usability of the study results have also been discussed.National Natural Science Foundation (China) (Grant Number: 61471398)Beijing Natural Science Foundation (Grant Number: 3122034)General Logistics Science Foundation (Grant Number: CWS11C108)National Key Technology Research and Development Program (Grant Numbers: 2013BAI03B04, 2013BAI03B05

Diagnosis-guided method for identifying multi-modality neuroimaging biomarkers associated with genetic risk factors in Alzheimer’s disease

Many recent imaging genetic studies focus on detecting the associations between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs). Although there exist a large number of generalized multivariate regression analysis methods, few of them have used diagnosis information in subjects to enhance the analysis performance. In addition, few of models have investigated the identification of multi-modality phenotypic patterns associated with interesting genotype groups in traditional methods. To reveal disease-relevant imaging genetic associations, we propose a novel diagnosis-guided multi-modality (DGMM) framework to discover multi-modality imaging QTs that are associated with both Alzheimer's disease (AD) and its top genetic risk factor (i.e., APOE SNP rs429358). The strength of our proposed method is that it explicitly models the priori diagnosis information among subjects in the objective function for selecting the disease-relevant and robust multi-modality QTs associated with the SNP. We evaluate our method on two modalities of imaging phenotypes, i.e., those extracted from structural magnetic resonance imaging (MRI) data and fluorodeoxyglucose positron emission tomography (FDG-PET) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results demonstrate that our proposed method not only achieves better performances under the metrics of root mean squared error and correlation coefficient but also can identify common informative regions of interests (ROIs) across multiple modalities to guide the disease-induced biological interpretation, compared with other reference methods

Identifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Disease

Neuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific. A useful, but underexplored, scenario could be to discover only those QTs associated with both genetic markers and disease status for revealing the chain from genotype to phenotype to symptom. In addition, multimodal brain imaging phenotypes are extracted from different perspectives and imaging markers consistently showing up in multimodalities may provide more insights for mechanistic understanding of diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a general framework to exploit multi-modal brain imaging phenotypes as intermediate traits that bridge genetic risk factors and multi-class disease status. We applied our proposed method to explore the relation between the well-known AD risk SNP APOE rs429358 and three baseline brain imaging modalities (i.e., structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that our proposed method not only helps improve the performances of imaging genetic associations, but also discovers robust and consistent regions of interests (ROIs) across multi-modalities to guide the disease-induced interpretation

Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis

Motivation: Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers. Results: The proposed TGSCCA method is able to capture the temporal changes in brain from longitudinal phenotypes by incorporating the fused penalty, which requires that the differences between two consecutive canonical weight vectors from adjacent time-points should be small. A new efficient optimization algorithm is designed to solve the objective function. Furthermore, we demonstrate the effectiveness of our algorithm on both synthetic and real data (i.e., the Alzheimer’s Disease Neuroimaging Initiative cohort, including progressive mild cognitive impairment, stable MCI and Normal Control participants). In comparison with conventional SCCA, our proposed method can achieve strong associations and discover phenotypic biomarkers across multiple time-points to guide disease-progressive interpretation

Wireless Real-Time Capacitance Readout Based on Perturbed Nonlinear Parity-Time Symmetry

In this article, we report a vector-network-analyzer-free and real-time LC wireless capacitance readout system based on perturbed nonlinear parity-time (PT) symmetry. The system is composed of two inductively coupled reader-sensor parallel RLC resonators with gain and loss respectively. By searching for the real mode that requires the minimum saturation gain, the steady-state frequency evolution as a function of the sensor capacitance perturbation is analytically deduced. The proposed system can work in different modes by setting different perturbation point. In particular, at the exceptional point of PT symmetry, the system exhibits high sensitivity. Experimental demonstrations revealed the viability of the proposed readout mechanism by measuring the steady-state frequency of the reader resonator in response to the change of trimmer capacitor on the sensor side. Our findings could impact many emerging applications such as implantable medical device for health monitoring, parameter detection in harsh environment and sealed food packages, etc

Genetic characterization and linkage disequilibrium mapping of resistance to gray leaf spot in maize (Zea mays L.)

AbstractGray leaf spot (GLS), caused by Cercospora zeae-maydis, is an important foliar disease of maize (Zea mays L.) worldwide, resistance to which is controlled by multiple quantitative trait loci (QTL). To gain insights into the genetic architecture underlying the resistance to this disease, an association mapping population consisting of 161 inbred lines was evaluated for resistance to GLS in a plant pathology nursery at Shenyang in 2010 and 2011. Subsequently, a genome-wide association study, using 41,101 single-nucleotide polymorphisms (SNPs), identified 51 SNPs significantly (P<0.001) associated with GLS resistance, which could be converted into 31 QTL. In addition, three candidate genes related to plant defense were identified, including nucleotide-binding-site/leucine-rich repeat, receptor-like kinase genes similar to those involved in basal defense. Two genic SNPs, PZE-103142893 and PZE-109119001, associated with GLS resistance in chromosome bins 3.07 and 9.07, can be used for marker-assisted selection (MAS) of GLS resistance. These results provide an important resource for developing molecular markers closely linked with the target trait, enhancing breeding efficiency

Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease

Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding

Identification and discovery of imaging genetic patterns using fusion self-expressive network in major depressive disorder

IntroductionMajor depressive disorder (MDD) is a prevalent mental illness, with severe symptoms that can significantly impair daily routines, social interactions, and professional pursuits. Recently, imaging genetics has received considerable attention for understanding the pathogenesis of human brain disorders. However, identifying and discovering the imaging genetic patterns between genetic variations, such as single nucleotide polymorphisms (SNPs), and brain imaging data still present an arduous challenge. Most of the existing MDD research focuses on single-modality brain imaging data and neglects the complex structure of brain imaging data.MethodsIn this study, we present a novel association analysis model based on a self-expressive network to identify and discover imaging genetics patterns between SNPs and multi-modality imaging data. Specifically, we first build the multi-modality phenotype network, which comprises voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. Then, we apply intra-class similarity information to construct self-expressive networks of multi-modality phenotype features via sparse representation. Subsequently, we design a fusion method guided by diagnosis information, which iteratively fuses the self-expressive networks of multi-modality phenotype features into a single new network. Finally, we propose an association analysis between MDD risk SNPs and the multi-modality phenotype network based on a fusion self-expressive network.ResultsExperimental results show that our method not only enhances the association between MDD risk SNP rs1799913 and the multi-modality phenotype network but also identifies some consistent and stable regions of interest (ROIs) multi-modality biological markers to guide the interpretation of MDD pathogenesis. Moreover, 15 new potential risk SNPs highly associated with MDD are discovered, which can further help interpret the MDD genetic mechanism.DiscussionIn this study, we discussed the discriminant and convergence performance of the fusion self-expressive network, parameters, and atlas selection