Feasibility of diffusion-tensor and correlated diffusion imaging for studying white-matter microstructural abnormalities: Application in COVID-19.

There has been growing attention on the effect of COVID-19 on white-matter microstructure, especially among those that self-isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single-shell diffusion magnetic resonance imaging (MRI) methods for detecting such effects. In this work, the performances of three single-shell-compatible diffusion MRI modeling methods are compared for detecting the effect of COVID-19, including diffusion-tensor imaging, diffusion-tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self-isolated patients at the study initiation and 3-month follow-up, along with age- and sex-matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single-shell methods to demonstrate COVID-19-related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID-19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID-19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID-19 related white-matter microstructural pathology manifests as a change in tissue diffusivity. Interestingly, different b-values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3-month follow-up, likely due to the limited size of the follow-up cohort. To summarize, correlated diffusion imaging is shown to be a viable single-shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID-19 patients, suggesting the two regions react differently to viral infection.

Tensor-based insights into systems immunity and infectious disease.

Profiling immune responses across several dimensions, including time, patients, molecular features, and tissue sites, can deepen our understanding of immunity as an integrated system. These studies require new analytical approaches to realize their full potential. We highlight recent applications of tensor methods and discuss several future opportunities.

Multidimensional Signal Processing and Applications—New Approaches

This chapter is a short introduction in the contemporary approaches aimed at the multidimensional processing and analysis of various kinds of signals, investigated in related research works, which were presented at the Third International Workshop "New Approaches for Multidimensional Signal Processing”, (NAMSP), held at the Technical University of Sofia, Bulgaria in July 2022. Some of the works cover various topics, as: moving objects tracking in video sequences, automatic audio classification, representation of color video чpeз 2-level tensor spectrum pyramid, etc., and also introduce multiple applications of the kind: analysis of electromyography signals, diagnostics of COVID based on ECG, etc. Short descriptions are given for the main themes covered by the book, which comprises the following three sections: multidimensional signal processing;applications of multidimensional signal processing, and applications of blockchain and network technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Multidimensional Signal Processing and Applications—New Approaches

This chapter is a short introduction in the contemporary approaches aimed at the multidimensional processing and analysis of various kinds of signals, investigated in related research works, which were presented at the Third International Workshop "New Approaches for Multidimensional Signal Processing”, (NAMSP), held at the Technical University of Sofia, Bulgaria in July 2022. Some of the works cover various topics, as: moving objects tracking in video sequences, automatic audio classification, representation of color video чpeз 2-level tensor spectrum pyramid, etc., and also introduce multiple applications of the kind: analysis of electromyography signals, diagnostics of COVID based on ECG, etc. Short descriptions are given for the main themes covered by the book, which comprises the following three sections: multidimensional signal processing;applications of multidimensional signal processing, and applications of blockchain and network technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Supply forecasting and profiling of urban supermarket chains based on tensor quantization exponential regression for social governance.

Background: During the COVID-19 pandemic, the accurate forecasting and profiling of the supply of fresh commodities in urban supermarket chains may help the city government make better economic decisions, support activities of daily living, and optimize transportation to support social governance. In urban supermarket chains, the large variety of fresh commodities and the short shelf life of fresh commodities lead to the poor performance of the traditional fresh commodity supply forecasting algorithm. Methods: Unlike the classic method of forecasting a single type of fresh commodity, we proposed a third-order exponential regression algorithm incorporating the block Hankle tensor. First, a multi-way delay embedding transform was used to fuse multiple fresh commodities sales to a Hankle tensor, for aggregating the correlation and mutual information of the whole category of fresh commodities. Second, high-order orthogonal iterations were performed for tensor decomposition, which effectively extracted the high-dimensional features of multiple related fresh commodities sales time series. Finally, a tensor quantization third-order exponential regression algorithm was employed to simultaneously predict the sales of multiple correlated fresh produce items. Results: The experiment result showed that the provided tensor quantization exponential regression method reduced the normalized root mean square error by 24% and the symmetric mean absolute percentage error by 22%, compared with the state-of-the-art approaches.

Multi-level optimization of the canonical polyadic tensor decomposition at large-scale: Application to the stratification of social networks deflation

Tensors are multi-dimensional mathematical objects that allow to model complex relationships and to perform decompositions for analytical purpose. They are used in a wide range of data mining applications. In social network analysis, tensor decompositions give interesting insights by taking into consideration multiple characteristics of data. However, the power-law distribution of such data forces the decomposition to reveal only the strong signals that hide information of interest having a lighter in-tensity. To reveal hidden information, we propose a method to stratify the signal, by gathering clusters of similar intensity in each stratum. It is an iterative process, in which the CANDECOMP/PARAFAC (CP) decomposition is applied and its result is used to deflate the tensor, i.e., by removing from the tensor the clusters found with the decomposition. As the CP decomposition is computationally demanding, it is also necessary to optimize its algorithm, to apply it on large-scale data with a reasonable execution time, even with the several executions needed by the iterative process of the stratification. Therefore, we propose an algorithm that uses both dense and sparse data structures and that leverages coarse and fine grained optimizations in addition to incremental computations in order to achieve large scale CP tensor decomposition. Our implementation outperforms the baseline of large-scale CP decomposition libraries by several orders of magnitude. We validate our stratification method and our optimized algorithm on a Twitter dataset about COVID vaccines.(c) 2022 Elsevier Ltd. All rights reserved.

Multi-level optimization of the canonical polyadic tensor decomposition at large-scale: Application to the stratification of social networks through deflation

Tensors are multi-dimensional mathematical objects that allow to model complex relationships and to perform decompositions for analytical purpose. They are used in a wide range of data mining applications. In social network analysis, tensor decompositions give interesting insights by taking into consideration multiple characteristics of data. However, the power-law distribution of such data forces the decomposition to reveal only the strong signals that hide information of interest having a lighter intensity. To reveal hidden information, we propose a method to stratify the signal, by gathering clusters of similar intensity in each stratum. It is an iterative process, in which the CANDECOMP/PARAFAC (CP) decomposition is applied and its result is used to deflate the tensor, i.e., by removing from the tensor the clusters found with the decomposition. As the CP decomposition is computationally demanding, it is also necessary to optimize its algorithm, to apply it on large-scale data with a reasonable execution time, even with the several executions needed by the iterative process of the stratification. Therefore, we propose an algorithm that uses both dense and sparse data structures and that leverages coarse and fine grained optimizations in addition to incremental computations in order to achieve large scale CP tensor decomposition. Our implementation outperforms the baseline of large-scale CP decomposition libraries by several orders of magnitude. We validate our stratification method and our optimized algorithm on a Twitter dataset about COVID vaccines.

Traffic Target Location Estimation Based on Tensor Decomposition in Intelligent Transportation System

As the safety problems and economic losses caused by traffic accidents are becoming more and more serious, intelligent transportation system (ITS) came into being. After the outbreak of COVID-19, how to achieve effective traffic scheduling and macro command under less contact has attracted more attention. Therefore, the location estimation of traffic objectives is a key issue. In the developed framework, for the target parameter estimation in traffic, frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is introduced into ITS, and tensor decomposition is used to process transportation big data (TBD) to improve the real-time performance of target location estimation. Unfortunately, spatial colored noise and array gain-phase error will affect the performance of FDA-MIMO radar in ITS. An algorithm that can solve the angle-range estimation problem of FDA-MIMO radar in the co-existence of array gain-phase error and spatial colored noise is proposed. Firstly, the four-dimensional tensor is constructed by using the temporal un-correlation of colored noise. Therefore, the influence of colored noise in ITS is removed. Secondly, the direction matrix containing target information is obtained by parallel factor (PARAFAC) decomposition. For the array gain-phase error, the optimization problem is constructed, and the Lagrange multiplier is employed to calculate the optimal solution. The effect of gain-phase error is eliminated by utilizing the optimal solution and the direction matrices. Finally, the location information of motor vehicle is achieved by calculating the solution of least square (LS) fitting. The developed scheme can achieve the location information of motor vehicles in the co-existence of array gain-phase error and spatial colored noise. Comprehensive numerical experiments illustrate that the developed scheme in ITS can efficiently obtain the location information of motor vehicles.

Link Prediction based on Tensor Decomposition for the Knowledge Graph of COVID-19 Antiviral Drug

Due to the large-scale spread of COVID-19, which has a significant impact on human health and social economy, developing effective antiviral drugs for COVID-19 is vital to saving human lives. Various biomedical associations, e.g., drug-virus and viral protein-host protein interactions, can be used for building biomedical knowledge graphs. Based on these sources, large-scale knowledge reasoning algorithms can be used to predict new links between antiviral drugs and viruses. To utilize the various heterogeneous biomedical associations, we proposed a fusion strategy to integrate the results of two tensor decomposition-based models (i.e., CP-N3 and ComplEx-N3). Sufficient experiments indicated that our method obtained high performance (MRR=0.2328). Compared with CP-N3, the mean reciprocal rank (MRR) is increased by 3.3% and compared with ComplEx-N3, the MRR is increased by 3.5%. Meanwhile, we explored the relationship between the performance and relationship types, which indicated that there is a negative correlation (PCC=0.446, P-value=2.26e-194) between the performance of triples predicted by our method and edge betweenness.

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea.

For a long time, various epidemics, such as lower respiratory infections and diarrheal diseases, have caused serious social losses and costs. Various methods for analyzing infectious disease occurrences have been proposed for effective prevention and proactive response to reduce such losses and costs. However, the results of the occurrence analyses were limited because numerous factors affect the outbreak of infectious diseases and there are complex interactions between these factors. To alleviate this limitation, we propose a cluster-based analysis scheme of infectious disease occurrences that can discover commonalities or differences between clusters by grouping elements with similar occurrence patterns. To do this, we collect and preprocess infectious disease occurrence data according to time, region, and disease. Then, we construct a tensor for the data and apply Tucker decomposition to extract latent features in the dimensions of time, region, and disease. Based on these latent features, we conduct k-means clustering and analyze the results for each dimension. To demonstrate the effectiveness of this scheme, we conduct a case study on data from South Korea and report some of the results.

Tensor-structured decomposition improves systems serology analysis.

Systems serology provides a broad view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease-relevant antigen targets, alongside additional measurements made for single antigens or in an antigen-generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Here, we report that coupled matrix-tensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV- and SARS-CoV-2-infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen-binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data exploration in systems serology.

Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus Can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection.

To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating COVID-19.