Recurrence-based time series analysis by means of complex network methods

Complex networks are an important paradigm of modern complex systems sciences which allows quantitatively assessing the structural properties of systems composed of different interacting entities. During the last years, intensive efforts have been spent on applying network-based concepts also for the analysis of dynamically relevant higher-order statistical properties of time series. Notably, many corresponding approaches are closely related with the concept of recurrence in phase space. In this paper, we review recent methodological advances in time series analysis based on complex networks, with a special emphasis on methods founded on recurrence plots. The potentials and limitations of the individual methods are discussed and illustrated for paradigmatic examples of dynamical systems as well as for real-world time series. Complex network measures are shown to provide information about structural features of dynamical systems that are complementary to those characterized by other methods of time series analysis and, hence, substantially enrich the knowledge gathered from other existing (linear as well as nonlinear) approaches.Comment: To be published in International Journal of Bifurcation and Chaos (2011

Advances in understanding of health-promoting benefits of medicine and food homology using analysis of gut microbiota and metabolomics

The health-promoting benefits of medicine and food homology (MFH) are known for thousands of years in China. However, active compounds and biological mechanisms are unclear, greatly limiting clinical practice of MFH. The advent of gut microbiota analysis and metabolomics emerge as key tools to discover functional compounds, therapeutic targets, and mechanisms of benefits of MFH. Such studies hold great promise to promote and optimize functional efficacy and development of MFH-based products, for example, foods for daily dietary supplements or for special medical purposes. In this review, we summarized pharmacological effects of 109 species of MFH approved by the Health and Fitness Commission in 2015. Recent studies applying genome sequencing of gut microbiota and metabolomics to explain the activity of MFH in prevention and management of health consequences were extensively reviewed. We discussed the potentiality in future to decipher functional activities of MFH by applying metabolomics-based polypharmacokinetic strategy and multiomics technologies. The needs for personalized MFH recommendations and comprehensive databases have also been highlighted. This review emphasizes current achievements and challenges of the analysis of gut microbiota and metabolomics as a new avenue to understand MFH

Control of Chromatin Organization and Chromosome Behavior during the Cell Cycle through Phase Separation

Phase-separated condensates participate in various biological activities. Liquid–liquid phase separation (LLPS) can be driven by collective interactions between multivalent and intrinsically disordered proteins. The manner in which chromatin—with various morphologies and activities—is organized in a complex and small nucleus still remains to be fully determined. Recent findings support the claim that phase separation is involved in the regulation of chromatin organization and chromosome behavior. Moreover, phase separation also influences key events during mitosis and meiosis. This review elaborately dissects how phase separation regulates chromatin and chromosome organization and controls mitotic and meiotic chromosome behavior

GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data

Electronic health records (EHRs) provide a rich repository to track a patient's health status. EHRs seek to fully document the patient's physiological status, and include data that is is high dimensional, heterogeneous, and multimodal. The significant differences in the sampling frequency of clinical variables can result in high missing rates and uneven time intervals between adjacent records in the multivariate clinical time-series data extracted from EHRs. Current studies using clinical time-series data for patient characterization view the patient's physiological status as a discrete process described by sporadically collected values, while the dynamics in patient's physiological status are time-continuous. In addition, recurrent neural networks (RNNs) models widely used for patient representation learning lack the perception of time intervals and velocity, which limits the ability of the model to represent the physiological status of the patient. In this paper, we propose an improved gated recurrent unit (GRU), namely time- and velocity-aware GRU (GRU-TV), for patient representation learning of clinical multivariate time-series data in a time-continuous manner. In proposed GRU-TV, the neural ordinary differential equations (ODEs) and velocity perception mechanism are used to perceive the time interval between records in the time-series data and changing rate of the patient's physiological status, respectively. Experimental results on two real-world clinical EHR datasets(PhysioNet2012, MIMIC-III) show that GRU-TV achieve state-of-the-art performance in computer aided diagnosis (CAD) tasks, and is more advantageous in processing sampled data

A comparative study of rigid and flexible MOFs for the adsorption of pharmaceuticals: Kinetics, isotherms and mechanisms

Recently metal-organic frameworks (MOFs) have attracted great attention in the field of environmental remediation. In this article, rigid MIL-101(Cr) and flexible MIL-53(Cr) were synthesized and used for the adsorption of two typical pharmaceuticals, clofibric acid (CA) and carbamazepine (CBZ), from water. The adsorption equilibrium was rapidly reached within 60 min and the kinetics best fitted with the pseudo-second-order kinetic model. There was no significant difference in the maximum adsorption capacity of CA on MIL-101(Cr) and MIL-53(Cr), and electrostatic interaction was suggested to be the main factor in the adsorption processes. However, for the removal of CBZ, MIL-53(Cr) showed much better adsorptive performance (0.428 mmol/g) than MIL-101(Cr) (0.0570 mmol/g), indicating the adsorption of CBZ on MOFs is affected by the structural property. The Powder X-ray diffraction analysis revealed that MIL-53(Cr) was transformed into large pore form, leading to variations in cell volume up to 33%, lower binding energy and crucial modifications of the hydrophobicity/hydrophilicity. This unusual behavior enhanced its adsorption capacity for CBZ. Moreover, hydrogen bonding and π-π interactions/stacking also contributed to the adsorption of pharmaceuticals on the MOFs. The excellent adsorptive performance of MIL-53(Cr) and its structure/property switching might lead to the applications in water treatment

A comparative study of rigid and flexible MOFs for the adsorption of pharmaceuticals: Kinetics, isotherms and mechanisms

Recently metal-organic frameworks (MOFs) have attracted great attention in the field of environmental remediation. In this article, rigid MIL-101(Cr) and flexible MIL-53(Cr) were synthesized and used for the adsorption of two typical pharmaceuticals, clofibric acid (CA) and carbamazepine (CBZ), from water. The adsorption equilibrium was rapidly reached within 60 min and the kinetics best fitted with the pseudo-second-order kinetic model. There was no significant difference in the maximum adsorption capacity of CA on MIL-101(Cr) and MIL-53(Cr), and electrostatic interaction was suggested to be the main factor in the adsorption processes. However, for the removal of CBZ, MIL-53(Cr) showed much better adsorptive performance (0.428 mmol/g) than MIL-101(Cr) (0.0570 mmol/g), indicating the adsorption of CBZ on MOFs is affected by the structural property. The Powder X-ray diffraction analysis revealed that MIL-53(Cr) was transformed into large pore form, leading to variations in cell volume up to 33%, lower binding energy and crucial modifications of the hydrophobicity/hydrophilicity. This unusual behavior enhanced its adsorption capacity for CBZ. Moreover, hydrogen bonding and π-π interactions/stacking also contributed to the adsorption of pharmaceuticals on the MOFs. The excellent adsorptive performance of MIL-53(Cr) and its structure/property switching might lead to the applications in water treatment

Disentangled Dynamic Deviation Transformer Networks for Multivariate Time Series Anomaly Detection

Graph neural networks have been widely used by multivariate time series-based anomaly detection algorithms to model the dependencies of system sensors. Previous studies have focused on learning the fixed dependency patterns between sensors. However, they ignore that the inter-sensor and temporal dependencies of time series are highly nonlinear and dynamic, leading to inevitable false alarms. In this paper, we propose a novel disentangled dynamic deviation transformer network (D3TN) for anomaly detection of multivariate time series, which jointly exploits multiscale dynamic inter-sensor dependencies and long-term temporal dependencies to improve the accuracy of multivariate time series prediction. Specifically, to disentangle the multiscale graph convolution, we design a novel disentangled multiscale aggregation scheme to better represent the hidden dependencies between sensors to learn fixed inter-sensor dependencies based on static topology. To capture dynamic inter-sensor dependencies determined by real-time monitoring situations and unexpected anomalies, we introduce a self-attention mechanism to model dynamic directed interactions in various potential subspaces influenced by various factors. In addition, complex temporal correlations across multiple time steps are simulated by processing the time series in parallel. Experiments on three real datasets show that the proposed D3TN significantly outperforms the state-of-the-art methods

3D laser scanning applied in conjunction with BIM: A fast and automated approach to inverse modeling

Nowadays, 3D laser scanning technology is increasingly applied to the construction industry as a means of collecting real-life building information, which is highly accurate, fast, and visualized, and can improve the efficiency and quality of construction projects, saving time and cost. The integration of 3D laser scanning and BIM technology generally requires reverse modeling of the point cloud. There are many ways to reverse model a 3D model from a point cloud model, but how to reverse model more efficiently is still the current research direction. In this paper, we propose a method for reverse modeling of point cloud by combining 3D laser scanning technology and Dynamo visual programming platform, which can perform rapid and automatic reverse modeling of shaped components and add family libraries, which can be retrieved at any time in the subsequent projects and make secondary edits to the constructed model