261 Accelerometer-Derived Sleep and Circadian Domains and Sociodemographic Correlates in the UK Biobank

Introduction Sleep health encompasses sleep regularity, duration, timing, efficiency and satisfaction. Accelerometry is an established method to estimate sleep in ecologically valid contexts, capturing key characteristic of rest-activity patterns, and facilitating population sleep health research. While hundreds of traits can be generated from open-source algorithms applied to raw acceleration data, the lack of clarity around their meaningful use beyond conventional measures limit the ability of these data to systematically inform evidence-based practices promoting sleep health. Here, we propose a method to identify key sleep and circadian domains, using data reduction methods for hundreds of accelerometer-derived traits to inform population-based sleep heath research. We also aimed to validate our findings by assessing whether the identified domains captured known sociodemographic associations. Methods We analyzed sociodemographic and raw triaxial accelerometer data recorded for 7 days from 79,876 adults (mean age 56.3±2.1 years, 56.3% women) participating in the UK Biobank. Standardized data processing using the open-source package GGIR (v1.7-1) resulted in the generation of 107 sleep and circadian traits. Variable clustering was used to identify key sleep and circadian domains, pertinent to sleep health, representing interpretable biological constructs minimizing correlation with other domains. Associations between identified domains and sociodemographic factors were evaluated using general linear models, and clinically significant differences were determined by standardized mean differences (SMD) ≥0.3. Results We identified 25 sleep and circadian domains explaining ≥80% of the variability of all 107 included traits. Domains capturing measures of variability tended to cluster together. The most clinically significant associations with sociodemographic characteristics were: women (vs. men) had higher sleep efficiency and lower accumulation of diurnal sleep periods; older (vs. younger) individuals had earlier most active starting time, lower acceleration amplitude and lower number of nocturnal sleep periods; and shift (vs. non-shift) workers had higher variability in sleep timing on weekends. Conclusion We demonstrate that variable clustering on accelerometer-derived data can identify meaningful sleep and circadian domains. In addition, identified domains captured known sociodemographic associations commonly observed in the sleep and circadian literature, suggesting that they could be relevant to inform public health practices that promote sleep health. Support (if any) NHLBI 5R01HL143790-02(PG); NIMHHD R01MD012734(FP); NIDA R01DA051321(FP); NIH/NHLBI K01HL123612(JM)

Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components

The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables.

Synergy of calcium release site determinants in control of calcium release events in cardiac myocytes

Recent data on structure of dyads in cardiac myocytes indicate variable clustering of RyR calcium release channels. The question arises as to how geometric factors of RyR arrangement translate to their role in formation of calcium release events (CRE). Since this question is not experimentally testable in situ, we performed in silico experiments on a large set of calcium release site (CRS) models. The models covered the range of RyR spatial distributions observed in dyads, and included gating of RyRs with open probability dependent on Ca2+ and Mg2+ concentration. The RyR single-channel calcium current, varied in the range of previously reported values, was set constant in the course of CRE simulations. Other known features of dyads were omitted in the model formulation for clarity. CRE simulations initiated by a single random opening of one of the RyRs in a CRS produced spark-like responses with characteristics that varied with RyR vicinity, a newly defined parameter quantifying spatial distribution of RyRs in the CRSs, and with the RyR single-channel calcium current. The CRE characteristics followed the law of mass action with respect to a CRS state variable, defined as a weighed product of RyR vicinity and RyR single-channel calcium current. The results explained the structure-function relations among determinants of cardiac dyads on synergy principles and thus allowed to evolve the concept of CRS as a dynamic unit of cardiac dyad.

A PRIMARY EXPLORATION ON THE SUPER-NETWORK MODEL OF ECONOMIC DEVELOPMENT UNDER THE RESTRICTION OF RESOURCES AND ENVIRONMENT

With the theory of super-network and the variational inequality method, this paper establishes a super-network model of economic development in three aspects, builds a new indicator system for measuring economic development, and calculates the equilibrium conditions for the balance of economic development. Firstly, the economic development is divided into three parts: economic growth, ecological environment optimization and scientific and technological progress. Respectively, with the set of measuring indicators in three aspects as a point of super-network and the link between these indicators as a super edge, thus a super-network model is established. Secondly, the hierarchical variable clustering analysis method is used to analyze the tightness of the links among the nodes in the super-network; and the objective function is established by using the partial measurement index, which simplifies the model. Finally, the variational inequality and projection iterative algorithm are used to calculate the equilibrium condition of the whole model. The main conclusions in this paper are as follows: (1) The indicators of economic development are interrelated with each other. (2) It can contribute to the balanced development of the economy in satisfied certain conditions.

Various Statistical Approaches to Assess and Predict Carcass and Meat Quality Traits

The beef industry is organized around different stakeholders, each with their own expectations, sometimes antagonistic. This article first outlines these differing perspectives. Then, various optimization models that might integrate all these expectations are described. The final goal is to define practices that could increase value for animal production, carcasses and meat whilst simultaneously meeting the main expectations of the beef industry. Different models previously developed worldwide are proposed here. Two new computational methodologies that allow the simultaneous selection of the best regression models and the most interesting covariates to predict carcass and/or meat quality are developed. Then, a method of variable clustering is explained that is accurate in evaluating the interrelationships between different parameters of interest. Finally, some principles for the management of quality trade-offs are presented and the Meat Standards Australia model is discussed. The “Pareto front” is an interesting approach to deal jointly with the different sets of expectations and to propose a method that could optimize all expectations together.

Memetic Variable Clustering and Its Application

Clustering analysis is an important and difficult task in data mining and big data analysis. Although being a widely used clustering analysis technique, variable clustering did not get enough attention in previous studies. Inspired by the metaheuristic optimization techniques developed for clustering data items, we try to overcome the main shortcoming of k-means-based variable clustering algorithm, which is being sensitive to initial centroids by introducing the metaheuristic optimization. A novel memetic algorithm named MCLPSO (Memetic Comprehensive Learning Particle Swarm Optimization) based on CLPSO (Comprehensive Learning Particle Swarm Optimization) has been studied under the framework of memetic computing in our previous work. In this work, MCLPSO is used as a metaheuristic approach to improve the k-means-based variable clustering algorithm by adjusting the initial centroids iteratively to maximize the homogeneity of the clustering results. In MCLPSO, a chaotic local search operator is used and a simulated annealing- (SA-) based local search strategy is developed by combining the cognition-only PSO model with SA. The adaptive memetic strategy can enable the stagnant particles which cannot be improved by the comprehensive learning strategy to escape from the local optima and enable some elite particles to give fine-grained local search around the promising regions. The experimental result demonstrates a good performance of MCLPSO in optimizing the variable clustering criterion on several datasets compared with the original variable clustering method. Finally, for practical use, we also developed a web-based interactive software platform for the proposed approach and give a practical case study—analyzing the performance of semiconductor manufacturing system to demonstrate the usage.