Mining high-level brain imaging genetic associations

Indiana University-Purdue University Indianapolis (IUPUI)Imaging genetics is an emerging research field in neurodegenerative diseases. It studies the influence of genetic variants on brain structure and function. Genome-wide association studies (GWAS) of brain imaging has identified a few independent risk loci for individual imaging quantitative trait (iQT), which however display only modest effect size and explain limited heritability. This thesis focuses on mining high-level imaging genetic associations and their applications on neurodegenerative diseases. This thesis first presents a novel network-based GWAS framework for identifying functional modules, by employing a two-step strategy in a top-down manner. It first integrates tissue-specific network with GWAS of corresponding phenotype in regression models in addition to classification, to re-prioritize genome-wide associations. Then it detects densely connected and disease-relevant modules based on interactions among top reprioritizations. The discovered modules hold both phenotypical specificity and densely interaction. We applied it to an amygdala imaging genetics analysis in the study of Alzheimer's disease (AD). The proposed framework effectively detects densely interacted modules; and the reprioritizations achieve highest concordance with AD genes. We then present an extension of the above framework, named GWAS top-neighbor-based (tnGWAS); and compare it with previous approaches. This tnGWAS extracts densely connected modules from top GWAS findings, based on the hypothesis that relevant modules consist of top GWAS findings and their close neighbors. It is applied to a hippocampus imaging genetics analysis in AD research, and yields the densest interactions among top candidate genes. Experimental results demonstrate that precise context does help explore collective effects of genes with functional interactions specific to the studied phenotype. In the second part, a novel imaging genetic enrichment analysis (IGEA) paradigm is proposed for discovering complex associations among genetic modules and brain circuits. In addition to genetic modules, brain regions of interest also grouped to play role. We expand the scope of one-dimensional enrichment analysis into imaging genetics. This framework jointly considers meaningful gene sets (GS) and brain circuits (BC), and examines whether given GS-BC module is enriched in gene-iQT findings. We conduct the proof-of-concept study and demonstrate its performance by applying to a brain-wide imaging genetics study of AD

Research on the “Phenomenon of Isolated Island” of Modern Society Development and the Governing Pathways of Enterprise Business Environment

With the development of society, various “phenomenon of isolated island” appear in social activities, such as “Scenic spot isolated island”, “cultural isolated island”, “economic isolated island”, “market isolated island”, “information isolated island” and so on. “Phenomenon of isolated island” causes a series of harm to the society, such as “system isolated island”, “business isolated island”, “data isolated island”, “management and control isolated island”, etc. Each department operates independently, resources and information cannot be shared, which increases the internal communication cost of enterprises and affects the work efficiency. It has a negative impact on both social and economic development. It is necessary to study the isolated phenomenon of isolated island in depth.The main reason for the “phenomenon of isolated island” is that there is no mechanism of interest sharing and action coordination among the cooperative subjects. There are both internal and external reasons. From the formation process, the interaction of technology, policy, economy, culture and other factors is the main reason for the formation of “phenomenon of isolated island”.In view of this situation, this paper explains the meaning of “phenomenon of isolated island”, analyzes the generation, existing problems and harm of “phenomenon of isolated island”, summarizes the research status of “phenomenon of isolated island”, and finally puts forward countermeasures and paths of “phenomenon of isolated island”, hoping to provide helpful help to social governance and economic development

Effect of Bridge-Pier Differential Settlement on the Dynamic Response of a High-Speed Railway Train-Track-Bridge System

A model based on the theory of train-track-bridge coupling dynamics is built in the article to investigate how high-speed railway bridge pier differential settlement can affect various railway performance-related criteria. The performance of the model compares favorably with that of a 3D finite element model and train-track-bridge numerical model. The analysis of the study demonstrates that all the dynamic response for a span of 24 m is slightly larger than that for a span of 32 m. The wheel unloading rate increases with pier differential settlement for all of the calculation conditions considered, and its maximum value of 0.695 is well below the allowable limit. Meanwhile, the vertical acceleration increases with pier differential settlement and train speed, respectively, and the values for a pier differential settlement of 10 mm and speed of 350 km/h exceed the maximum allowable limit stipulated in the Chinese standards. On this basis, a speed limit for the exceeding pier differential settlement is determined for comfort consideration. Fasteners that had an initial tensile force due to pier differential settlement experience both compressive and tensile forces as the train passes through and are likely to have a lower service life than those which solely experience compressive forces

Chemical fingerprinting and quantitative analysis of a Panax notoginseng preparation using HPLC-UV and HPLC-MS

<p>Abstract</p> <p>Background</p> <p>Xuesaitong (XST) injection, consisting of total saponins from <it>Panax notoginseng</it>, was widely used for the treatment of cardio- and cerebro-vascular diseases in China. This study develops a simple and global quality evaluation method for the quality control of XST.</p> <p>Methods</p> <p>High performance liquid chromatography-ultraviolet detection (HPLC-UV) was used to identify and quantify the chromatographic fingerprints of the XST injection. Characteristic common peaks were identified using HPLC with photo diode array detection/electrospray ionization tandem mass spectrometry (HPLC-PDA/ESI-MSⁿ).</p> <p>Results</p> <p>Representative fingerprints from ten batches of samples showed 27 'common saponins' all of which were identified and quantified using ten reference saponins.</p> <p>Conclusion</p> <p>Chemical fingerprinting and quantitative analysis identified most of the common saponins for the quality control of <it>P. notoginseng </it>products such as the XST injection.</p

Crystal metamorphosis at stress extremes: how soft phonons turn into lattice defects

r The Author(s) 2016 At 0 K, phonon instability controls the ideal strength and the ultrafast dynamics of defect nucleation in perfect crystals under high stress. However, how a soft phonon evolves into a lattice defect is still unclear. Here, we develop a full-Brillouin zone soft-phonon-searching algorithm that shows outstanding accuracy and efficiency for pinpointing general phonon instability within the joint material-reciprocal (x–k) spaces. By combining finite-element modeling with embedded phonon algorithm and atomistic simulation, we show how a zone-boundary soft phonon is first triggered in a simple metal (aluminum) under nanoindentation, subsequently leading to a transient new crystal phase and ensuing nucleation of a deformation twin with only one-half of the transformation strain of the conventional twin. We propose a two-stage mechanism governing the transformation of unstable shortwave phonons into lattice defects, which is fundamentally different from that initially triggered by soft long-wavelength phonons. The uncovered material dynamics at stress extremes reveal deep connections between delocalized phonons and localized defects trapped by the full nonlinear potential energy landscape and add to the rich repertoire of nonlinear dynamics found in nature.National Natural Science Foundation of China (Grant No. 50971090)National Natural Science Foundation of China (Grant No. 51071101)National Natural Science Foundation of China (Grant No. 51471107)National Science Foundation (U.S.). Division of Materials Research (DMR-410636

Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

Motivation Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted. Results We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression. Availability and implementation The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA. Supplementary information Supplementary data are available at Bioinformatics online

The New Extended Family: The Experience of Parents and Children after Remarriage

During the past 2 decades, the nuclear family, the predominant family form in the United States, has appeared to be more ephemeral than was once imagined by social scientists. Historians and demographers have shown that this family form was not nearly so common in earlier times as was once thought (Cherlin, 1981; Hareven, 1978). Paradoxically the nuclear family (ironically, now referred to as the traditional family) was more common in 1950 than in 1850 because of high rates of mortality, illness, and economic uncertainty (Uhlenberg, 1974). Large numbers of people never married or never had children, and among those who did, the prospect of living a settled and secure life was much lower than is nostalgically recalled

Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics

Brain imaging genetics studies the genetic basis of brain structures and functions via integrating both genotypic data such as single nucleotide polymorphism (SNP) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few of QTs and are not designed for feature selection from a large number of QTs; while existing SCCA methods typically employ only one modality of QTs to study its association with SNPs. Both MTL and SCCA encounter computational challenges as the number of SNPs increases. In this paper, combining the merits of MTL and SCCA, we propose a novel multi-task SCCA (MTSCCA) learning framework to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. Using the G2,1-norm regularization, MTSCCA treats all SNPs in the same group together to enforce sparsity at the group level. The l2,1-norm penalty is used to jointly select features across multiple tasks for SNPs, and across multiple modalities for QTs. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains improved performance regarding both correlation coefficients and canonical weights patterns. In addition, our method runs very fast and is easy-to-implement, and thus could provide a powerful tool for genome-wide brain-wide imaging genetic studies