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

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

Flash Flood Early Warning Research in China

Along with global climate change, extreme rainfall causes severe flash flood disasters, especially in mountainous areas. As about 67% of the terrestrial part of the whole country is mountain area with frequent heavy rainfall, China suffers from flash flood disasters throughout its history. As flash floods are distributed extensively and its influence sphere highly concentrated, it is unreasonable and uneconomical to prevent flash flood disasters mainly via engineering measures. Then, China starts exploring about flash flood early warning, which is optimal for developing country with dense populations, since the 1990s. Based on the literature research, a systematic framework for Chinese flash flood early warning research has been developed. In this frame, flash flood early warning is classified into long-term warning and real-time warning. This chapter presents the Chinese achievements in analysis methods for long-term warning, computational methods for real-time warning indicators, improving data sources used for real-time warnings and the information construction of real-time warning systems. In addition, the suggestions for future study are presented

Research on Cooperative Innovation Behavior of Industrial Cluster Based on Subject Adaptability

From the perspective of the interactive cooperation among subjects, this paper portrays the process of cooperative innovation in industrial cluster, in order to capture the correlated equilibrium relationship among them. Through the utilization of two key tools, evolutionary stable strategy and replicator dynamics equations, this paper considers the cost and gains of cooperative innovation and the amount of government support as well as other factors to build and analyze a classic evolutionary game model. On this basis, the subject’s own adaptability is introduced, which is regarded as the system noise in the stochastic evolutionary game model so as to analyze the impact of adaptability on the game strategy selection. The results show that, in the first place, without considering subjects’ adaptability, their cooperation in industrial clusters depends on the cost and gains of innovative cooperation, the amount of government support, and some conditions that can promote cooperation, namely, game steady state. In the second place after the introduction of subjects’ adaptability, it will affect both game theory selection process and time, which means that the process becomes more complex, presents the nonlinear characteristics, and helps them to make faster decisions in their favor, but the final steady state remains unchanged

Simulation Technology for Hydrodynamic and Water Quality in the Main Canal

The hydrodynamic and water quality simulation technology can be used for predicting the pollutant diffusion process after a sudden water pollution accident, and for analyzing the effect of emergency operation measures. The MRP features a long route, a variety of buildings, etc.; therefore, a set of hydrodynamic and water quality models that are applicable to the main canal of the MRP was independently developed based on 1-D open canal hydrodynamic and water quality theory and with various types of buildings as inner boundaries. Through calibration and verification, these models can be applied to the simulation of hydraulic and water quality response process under any operation conditions in the main canal of the MRP

Emergency Operations of Sudden Water Pollution Accidents

Emergency operation technologies can help to make reasonable operation measures of hydraulic structures, which are important to control the scope of the effect arising from an event and reduce the harm caused thereby. The main canal of MRP is divided into three parts in case of sudden water pollution accidents: the accident pool, the upstream section of the accident pool, and the downstream section of the accident pool. For each part, the target and strategy for emergency operation technologies are discussed. With regard to an accident pool, multiple kinds of check gate closing methods, synchronous, asynchronous, identical speed, and different speed are put forward; for the upstream section, a new method of equal-volume operation is introduced; and for the downstream section, three emergency operation methods are proposed. The simulation result of case study shows that the methods raised in this chapter can be used to determine suitable emergency operation measures