3D Tensor Research Articles

DOGpred: A Novel Deep Learning Framework for Accurate Identification of Human O-linked Threonine Glycosylation Sites.

O-linked glycosylation is a crucial post-transcriptional modification that regulates protein function and biological processes. Dysregulation of this process is associated with various diseases, underscoring the need to accurately identify O-linked glycosylation sites on proteins. Current experimental methods for identifying O-linked threonine glycosylation (OTG) sites are often complex and costly. Consequently, developing computational tools that predict these sites based on protein features is crucial. Such tools can complement experimental approaches, enhancing our understanding of the role of OTG dysregulation in diseases and uncovering potential therapeutic targets. In this study, we developed DOGpred, a deep learning-based predictor for precisely identifying human OTGs using high-latent feature representations. Initially, we extracted nine different conventional feature descriptors (CFDs) and nine pre-trained protein language model (PLM)-based embeddings. Notably, each feature was encoded as a 2D tensor, capturing both the sequential and inherent feature characteristics. Subsequently, we designed a stacked convolutional neural network (CNN) module to learn spatial feature representations from CFDs and a stacked recurrent neural network (RNN) module to learn temporal feature representations from PLM-based embeddings. These features were integrated using attention-based fusion mechanisms to generate high-level feature representations for final classification. Ablation analysis and independent tests demonstrated that the optimal model (DOGpred), employing a stacked 1D CNN and a stacked attention-based RNN module with cross-attention feature fusion, achieved the best performance on the training dataset and significantly outperformed machine learning-based single-feature models and state-of-the-art methods on independent datasets. Furthermore, DOGpred is publicly available at https://github.com/JeonRPM/DOGpred/ for free access and usage.

Histological Validation of Multi-Echo Gradient Echo (MGRE)-Derived Myelin Water Fraction (MWF) at 9.4 T and the Influence of Orientation on Quantification.

Myelin is essential in the nervous system of mammals. As the location and degree of myelin loss can reflect varied pathophysiological status, noninvasive measurement of myelin is of high importance. The magnetic resonance imaging (MRI) technique of myelin water fraction (MWF) derived from multi-echo gradient echo (MGRE) sequence is a promising tool for the quantification of myelin content due to the low specific absorption rate (SAR) compared with the spin-echo sequence, time efficiency, and wide availability. Yet to our knowledge, MGRE-derived MWF has never been quantitatively validated with histology. The main objective of this study was to quantitatively validate the MRI findings by referencing the myelin histology using a rat model. As a second objective, we investigated how the orientation of white matter fibers with respect to the static B0 field impacted both the apparent transverse relaxation rate (R2* = 1/T2*) and the derived MWF. Moreover, MWF is known to change with age; thus, we compared rat brains of different ages. The orientation effect of MWF in a clinical setting was studied using 3 T human data. Twenty exvivo rat brains with different ages and three healthy volunteers were scanned on a 9.4 T Bruker and 3.0 T Siemens systems, respectively. The 3D MGRE and diffusion tensor imaging (DTI) data were acquired. Our results showed a highly significant correlation between MGRE-derived MWF and histological stain of myelin, and susceptibility and diffusivity also demonstrated a significant association with myelin. Both MWF and R2* (R2* = 1/T2*) values changed as a function of orientation, and the function varied with age. Furthermore, MWF and R2* were more sensitive to age than DTI. Invivo 3 T human MWF also changed substantially with the orientation as well. Our results support that MGRE-derived MWF can be used to assess the myelin content quantitatively.

Filling the Gap: Enhancing Borehole Imaging with Tensor Neural Network

Borehole imaging is crucial in geological research as it offers insights into subsurface formations and supports reservoir assessment, mineral exploration, and hydrocarbon extraction. However, the effectiveness of borehole imaging is limited by the incompleteness of data due to the design constraints of borehole imaging tools. Missing areas in borehole images pose challenges to geologists. While existing methods, such as pattern filling and CNN-based techniques, show some efficacy, they often require a large amount of complete images for training. In recent years, unsupervised deep learning and tensor-based methods have gained attention for their ability to reconstruct missing or degraded geological images by leveraging the structural characteristics of images. In particular, tensor representations based on Tucker decomposition have shown strong capabilities in data completion. Inspired by this, we propose a novel self-supervised Tensor Neural Network (TNN) utilizing Tucker decomposition as our backbone. Since borehole images are wraped to 2D data, converting them into tensor representations is a critical step in leveraging the proposed tensor representation. To achieve this, we introduce the Adaptive Boundary-Detection Cropping with Augmentation algorithm, which adapts 2D images into 3D tensors. After interpolating the tensors using the proposed tensor network, we employ Adaptive Slice Concatenation with Replacement to restore the complete images from the enhanced tensors, ensuring that the tensor representation of 3D data is accurately depicted in the 2D images. The proposed TNN can be further enhanced by incorporating a structural regularizer. Actual data experiments demonstrate that our method effectively fills gaps in borehole images with higher clarity and detail. The completed images retain crucial geological features and textures, surpassing some of the existing self-supervised learning methods.

A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.

Protein-ligand interactions are the molecular basis of many important cellular activities, such as gene regulation, cell metabolism, and signal transduction. Protein-ligand binding affinity is a crucial metric of the strength of the interaction between the two, and accurate prediction of its binding affinity is essential for discovering drugs' new uses. So far, although many predictive models based on machine learning and deep learning have been reported, most of the models mainly focus on one-dimensional sequence and two-dimensional structural characteristics of proteins and ligands, but fail to deeply explore the detailed interaction information between proteins and ligand atoms in the binding pocket region of three-dimensional space. In this study, we introduced a novel 4D tensor feature to capture key interactions within the binding pocket and developed a three-dimensional convolutional neural network (CNN) model based on this feature. Using ten-fold cross-validation, we identified the optimal parameter combination and pocket size. Additionally, we employed feature engineering to extract features across multiple dimensions, including one-dimensional sequences, two-dimensional structures of the ligand and protein, and three-dimensional interaction features between them. We proposed an efficient protein-ligand binding affinity prediction model MCDTA (multi-dimensional convolutional drug-target affinity), built on a multi-dimensional convolutional neural network framework. Feature ablation experiments revealed that the 4D tensor feature had the most significant impact on model performance. MCDTA performed exceptionally well on the PDBbind v.2020 dataset, achieving an RMSE of 1.231 and a PCC of 0.823. In comparative experiments, it outperformed five other mainstream binding affinity prediction models, with an RMSE of 1.349 and a PCC of 0.795. Moreover, MCDTA demonstrated strong generalization ability and practical screening performance across multiple benchmark datasets, highlighting its reliability and accuracy in predicting protein-ligand binding affinity. The code for MCDTA is available at https://github.com/dfhuang-AI/MCDTA .

DeepTensor: Low-Rank Tensor Decomposition With Deep Network Priors.

DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks. We decompose a tensor as the product of low-rank tensor factors (e.g., a matrix as the outer product of two vectors), where each low-rank tensor is generated by a deep network (DN) that is trained in a self-supervised manner to minimize the mean-square approximation error. Our key observation is that the implicit regularization inherent in DNs enables them to capture nonlinear signal structures (e.g., manifolds) that are out of the reach of classical linear methods like the singular value decomposition (SVD) and principal components analysis (PCA). Furthermore, in contrast to the SVD and PCA, whose performance deteriorates when the tensor's entries deviate from additive white Gaussian noise, we demonstrate that the performance of DeepTensor is robust to a wide range of distributions. We validate that DeepTensor is a robust and computationally efficient drop-in replacement for the SVD, PCA, nonnegative matrix factorization (NMF), and similar decompositions by exploring a range of real-world applications, including hyperspectral image denoising, 3D MRI tomography, and image classification. In particular, DeepTensor offers a 6 dB signal-to-noise ratio improvement over standard denoising methods for signal corrupted by Poisson noise and learns to decompose 3D tensors 60 times faster than a single DN equipped with 3D convolutions.

Bio-inspired multi-dimensional deep fusion learning for predicting dynamical aerospace propulsion systems

Rapid and precise forecasting of dynamical systems is critical to ensuring safe aerospace missions. Previous forecasting research has primarily concentrated on global trend analysis using full-scale inputs. However, time series arising from real-world applications such as aerospace propulsion, exhibit a distinct dynamical periodicity over a limited timeframe. Here we develop a deep learning model, TimeWaves, to capture both global trends and local variations, through 3D spectrum-oriented interval extraction from an integrated viewpoint of biological perceptions. Specifically, a shared parameter fusion algorithm is employed to effectively integrate Fourier and Wavelet analyses, providing full and sliced 1D sequences to form 2D tensors that can be seamlessly processed by parameter-efficient inception blocks. Additionally, a dual-way learning workflow using TwinBlock, inspired by the cooperative behavior of visual cells, is implemented to enhance perception of dynamical multi-scale features at a reduced computational cost. TimeWaves demonstrates reliable and robust performance in predicting rocket combustion instability, a key challenge in the aerospace industry.

On a fully three-dimensional bending analysis of very thick smart composite cube-like bulk structures

Here we discuss the behaviour of very thick composite plates considering electro-magneto-elastic coupling of various types using fully three-dimensional (3D) kinematics. Published research highlights a lack of studies on the 3D mechanics of smart composite plates that integrate both higher-order (flexoelectric/flexomagnetic) and lower-order (piezoelectric/piezomagnetic) multiple physical fields (electro-magneto-elastic). The common approach to achieving the targeted and desired mechanical behavior within such composites could involve using structural elements. This gap can potentially be addressed by amalgamating the term ∂/∂z with the 2D governing equations of plates. This expression indicates alterations in thickness, in which z is the coordinate dedicated to the thickness. The governing equations can be created by operating on the variational method which enables us to establish and settle the 3D bending equations of the bulk structure. The pointed-out equations have been influenced by the implementation of additional hypotheses, such as von Kármán’s strain and complicated 3D tensor relations. Inserting the term ∂/∂z into the mathematical model renders that the analytical solution techniques are unable to assist us in obtaining numerical results. Consequently, a semi-analytical solving method grounded on the polynomial phrases facilitates the acquisition of the required solution. This fully 3D bending study of very thick piezocomposite cube-like bulk structures (CBS) can be an original reference in the field of mechanics of intelligent plate-like structures.

TMAA-net: tensor-domain multi-planal anti-aliasing network for sparse-view CT image reconstruction

Objective.Among various deep-network-based sparse-view CT image reconstruction studies, the sinogram upscaling network has been predominantly employed to synthesize additional view information. However, the performance of the sinogram-based network is limited in terms of removing aliasing streak artifacts and recovering low-contrast small structures. In this study, we used a view-by-view back-projection (VVBP) tensor-domain network to overcome such limitations of the sinogram-based approaches.Approach.The proposed method offers advantages of addressing the aliasing artifacts directly in the 3D tensor domain over the 2D sinogram. In the tensor-domain network, the multi-planal anti-aliasing modules were used to remove artifacts within the coronal and sagittal tensor planes. In addition, the data-fidelity-based refinement module was also implemented to successively process output images of the tensor network to recover image sharpness and textures.Main result.The proposed method showed outperformance in terms of removing aliasing artifacts and recovering low-contrast details compared to other state-of-the-art sinogram-based networks. The performance was validated for both numerical and clinical projection data in a circular fan-beam CT configuration.Significance.We observed that view-by-view aliasing artifacts in sparse-view CT exhibit distinct patterns within the tensor planes, making them effectively removable in high-dimensional representations. Additionally, we demonstrated that the co-domain characteristics of tensor space processing offer higher generalization performance for aliasing artifact removal compared to conventional sinogram-domain processing.

Cryo-X-Ray Phase Contrast Imaging Enables Combined 3D Structural Quantification and Nucleic Acid Analysis of Myocardial Biopsies.

Snap-frozen biopsies serve as a valuable clinical resource of archival material for disease research, as they enable a comprehensive array of downstream analyses to be performed, including extraction and sequencing of nucleic acids. Obtaining three-dimensional (3D) structural information before multi-omics is more challenging but can potentially allow for better characterization of tissues and targeting of clinically relevant cells. Conventional histological techniques are limited in this regard due to their destructive nature and the reconstruction artifacts produced by sectioning, dehydration, and chemical processing. These limitations are particularly notable in soft tissues such as the heart. In this study, the feasibility of using synchrotron-based cryo-X-ray phase contrast imaging (cryo-X-PCI) of snap-frozen myocardial biopsies is assessed and 3D structure tensor analysis of aggregated myocytes, followed by nucleic acid (DNA and RNA) extraction and analysis. It is shown that optimal sample preparation is the key driver for successful structural and nucleic acid preservation which is unaffected by the process of cryo-X-PCI. It is proposed that cryo-X-PCI has clinical value for 3D tissue analysis of cardiac and potentially non-cardiac soft tissue biopsies before nucleic acid investigation.

Response characteristics of 3D tensor CSAMT in axis anisotropic media

Response characteristics of 3D tensor CSAMT in axis anisotropic media

E-FNet: A EEG-fNIRS dual-stream model for Brain–Computer Interfaces

With the rapid advancements in Brain–Computer Interfaces (BCI) and multimodal technology, researchers are actively exploring the intricate domain of multimodal BCI. The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) in BCI systems has garnered significant attention due to the combination of strengths from both modalities and the overcoming of limitations inherent in standalone systems. In this study, the E-FNet architecture is introduced, a (2+1)D dual-stream model explicitly designed to process multimodal EEG and fNIRS data for brain–machine interface applications. By leveraging the (2+1)D architecture and adopting a bias-free design, E-FNet efficiently processes multimodal data. A unified 4D tensor representation enables the achievement of both temporal and spatial alignment of EEG and fNIRS signals. For Motor Imagery (MI) and Mental Arithmetic (MA) tasks, the early integration of EEG and fNIRS signals yields remarkable accuracy rates of 95.86% and 95.80% respectively, outperforming the accuracy obtained with EEG signals alone. Moreover, the minimal variability in results across subjects underscores the complementary effects achieved through the integration of different signals.

Time Sequence Deep Learning Model for Ubiquitous Tabular Data with Unique 3D Tensors Manipulation.

Although deep learning (DL) algorithms have been proved to be effective in diverse research domains, their application in developing models for tabular data remains limited. Models trained on tabular data demonstrate higher efficacy using traditional machine learning models than DL models, which are largely attributed to the size and structure of tabular datasets and the specific application contexts in which they are utilized. Thus, the primary objective of this paper is to propose a method to use the supremacy of Stacked Bidirectional LSTM (Long Short-Term Memory) deep learning algorithms in pattern discovery incorporating tabular data with customized 3D tensor modeling in feeding neural networks. Our findings are empirically validated using six diverse, publicly available datasets each varying in size and learning objectives. This paper proves that the proposed model based on time-sequence DL algorithms, which were generally described as inadequate when dealing with tabular data, yields satisfactory results and competes effectively with other algorithms specifically designed for tabular data. An additional benefit of this approach is its ability to preserve simplicity while ensuring fast model training also with large datasets. Even with extremely small datasets, models can be applied to achieve exceptional predictive results and fully utilize their capacity.

Inferring Gene Regulatory Networks from Single-Cell Time-Course Data Based on Temporal Convolutional Networks

Background: Time-course single-cell RNA sequencing (scRNA-seq) data represent dynamic gene expression values that change over time, which can be used to infer causal relationships between genes and construct dynamic gene regulatory networks (GRNs). However, most of the existing methods are designed for bulk RNA sequencing (bulk RNA-seq) data and static scRNA-seq data, and only a few methods, such as CNNC and DeepDRIM can be directly applied to time-course scRNA-seq data. Objective: This work aims to infer causal relationships between genes and construct dynamic gene regulatory networks using time-course scRNA-seq data. Methods: We propose an analytical method for inferring GRNs from single-cell time-course data based on temporal convolutional networks (scTGRN), which provides a supervised learning approach to infer causal relationships among genes. scTGRN constructs a 4D tensor representing gene expression features for each gene pair, then inputs the constructed 4D tensor into the temporal convolutional network to train and infer the causal relationship between genes. Results: We validate the performance of scTGRN on five real datasets and four simulated datasets, and the experimental results show that scTGRN outperforms existing models in constructing GRNs. In addition, we test the performance of scTGRN on gene function assignment, and scTGRN outperforms other models. Conclusion: The analysis shows that scTGRN can not only accurately identify the causal relationship between genes, but also can be used to achieve gene function assignment.

A novel data-driven 3D site characterisation method: Tucker decomposition-Bayesian compressive sensing and benchmarking study

ABSTRACT Site characterisation plays a pivotal role in geotechnical design and analysis. With advancements in machine learning and other digital technologies, data-driven site characterisation (DDSC) has garnered substantial interest in data-centric geotechnics. This paper proposes a novel and competitive 3D DDSC method, called Tucker decomposition-Bayesian compressive sensing (TD-BCS), which employs Tucker decomposition to factorise a 3D high-order tensor into a low-rank core tensor along with three associated factor matrices. The method comprises three key components: (1) 3D sparse representation using Tucker decomposition and discrete cosine transform; (2) Bayesian compressive sensing, and its algorithm implementation; and (3) Determination of three effective factor matrices. The proposed TD-BCS method is evaluated through its application to two benchmark examples, involving virtual ground and actual ground scenario based on real CPT data. And the benchmarking results demonstrate that the method not only yields accurate estimates with quantified uncertainty from sparse measurements but also exhibits high computational efficiency compared to other DDSC methods. Indeed, the TD-BCS method can be effectively applied to other 3D geotechnical engineering cases, and its framework is readily extendable to higher dimensions.

Data-driven retrieval of population-level EEG features and their role in neurodegenerative diseases.

Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer's disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10-20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer's disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer's Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59-0.91) and Alzheimer's disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer's subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.

Distance plus attention for binding affinity prediction

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pi $$\\end{document}-stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap.Scientific Contribution StatementThis study innovatively introducesdistance-based features to predict protein-ligand binding affinity, capitalizing onunique molecular interactions. Furthermore, the incorporation of protein sequencefeatures of specific residues enhances the model’s proficiency in capturing intricatebinding patterns. The predictive capabilities are further strengthened through theuse of a deep learning architecture with attention mechanisms, and an ensembleapproach, averaging the outputs of five models, is implemented to ensure robustand reliable predictions.

ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes.

Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein-protein complexes based on a deep convolutional neural network. Prediction is carried out for the spatial structures of complexes, presented in the format of a 4D tensor, which includes information about the location of atoms and their abilities to participate in various types of interactions realized in protein-protein and protein-peptide complexes. The effectiveness of the model was assessed both on an internal test data set containing complexes consisting of three or more molecules, as well as on an external test for the PPI-Affinity service. As a result, we managed to achieve the best prediction quality on these data sets among all the analyzed models: on the internal test, Pearson correlation R = 0.6, MAE = 1.60, on the external test, R = 0.55, MAE = 1.75. The open-source code, the trained ProBAN model, and the collected dataset are freely available at the following link https://github.com/EABogdanova/ProBAN.

EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition.

Mediation analysis assesses whether an exposure directly produces changes in cognitive behavior or is influenced by intermediate "mediators". Electroencephalographic (EEG) spectral measurements have been previously used as effective mediators representing diverse aspects of brain function. However, it has been necessary to collapse EEG measures onto a single scalar using standard mediation methods. In this article, we overcome this limitation and examine EEG frequency-resolved functional connectivity measures as a mediator using the full EEG cross-spectral tensor (CST). Since CST samples do not exist in Euclidean space but in the Riemannian manifold of positive-definite tensors, we transform the problem, allowing for the use of classic multivariate statistics. Toward this end, we map the data from the original manifold space to the Euclidean tangent space, eliminating redundant information to conform to a "compressed CST." The resulting object is a matrix with rows corresponding to frequencies and columns to cross spectra between channels. We have developed a novel matrix mediation approach that leverages a nuclear norm regularization to determine the matrix-valued regression parameters. Furthermore, we introduced a global test for the overall CST mediation and a test to determine specific channels and frequencies driving the mediation. We validated the method through simulations and applied it to our well-studied 50+-year Barbados Nutrition Study dataset by comparing EEGs collected in school-age children (5-11 years) who were malnourished in the first year of life with those of healthy classmate controls. We hypothesized that the CST mediates the effect of malnutrition on cognitive performance. We can now explicitly pinpoint the frequencies (delta, theta, alpha, and beta bands) and regions (frontal, central, and occipital) in which functional connectivity was altered in previously malnourished children, an improvement to prior studies. Understanding the specific networks impacted by a history of postnatal malnutrition could pave the way for developing more targeted and personalized therapeutic interventions. Our methods offer a versatile framework applicable to mediation studies encompassing matrix and Hermitian 3D tensor mediators alongside scalar exposures and outcomes, facilitating comprehensive analyses across diverse research domains.

Efficient tensor network simulation of IBM's largest quantum processors

We show how quantum-inspired 2D tensor networks can be used to efficiently and accurately simulate the largest quantum processors from IBM, namely Eagle (127 qubits), Osprey (433 qubits), and Condor (1121 qubits). We simulate the dynamics of a complex quantum many-body system—specifically, the kicked Ising experiment considered recently by IBM in Y. Kim , —using graph-based projected entangled pair states (gPEPS), which was proposed by some of us in . Our results show that simple tensor updates are already sufficient to achieve very large unprecedented accuracy with remarkably low computational resources for this model. Apart from simulating the original experiment for 127 qubits, we also extend our results to 433 and 1121 qubits, and for evolution times around eight times longer, thus setting a benchmark for the newest IBM quantum machines. We also report accurate simulations for infinitely many qubits. Our results show that gPEPS are a natural tool to efficiently simulate quantum computers with an underlying lattice-based qubit connectivity, such as all quantum processors based on superconducting qubits. Published by the American Physical Society 2024

Wearable 12-Lead ECG Acquisition Using a Novel Deep Learning Approach from Frank or EASI Leads with Clinical Validation.

The 12-lead electrocardiogram (ECG) is crucial in assessing patient decisions. However, portable ECG devices capable of acquiring a complete 12-lead ECG are scarce. For the first time, a deep learning-based method is proposed to reconstruct the 12-lead ECG from Frank leads (VX, VY, and VZ) or EASI leads (VES, VAS, and VAI). The innovative ECG reconstruction network called M2Eformer is composed of a 2D-ECGblock and a ProbDecoder module. The 2D-ECGblock module adaptively segments EASI leads into multi-periods based on frequency energy, transforming the 1D time series into a 2D tensor representing within-cycle and between-cycle variations. The ProbDecoder module aims to extract Probsparse self-attention and achieve one-step output for the target leads. Experimental results from comparing recorded and reconstructed 12-lead ECG using Frank leads indicate that M2Eformer outperforms traditional ECG reconstruction methods on a public database. In this study, a self-constructed database (10 healthy individuals + 15 patients) was utilized for the clinical diagnostic validation of ECG reconstructed from EASI leads. Subsequently, both the ECG reconstructed using EASI and the recorded 12-lead ECG were subjected to a double-blind diagnostic experiment conducted by three cardiologists. The overall diagnostic consensus among three cardiology experts, reaching a rate of 96%, indicates the significant utility of EASI-reconstructed 12-lead ECG in facilitating the diagnosis of cardiac conditions.