Explore the vast range of titles available.

Spatial domain identification is crucial in spatial transcriptomics analysis. Existing methods excel with continuous and clustered distributions but struggle with discrete ones. We present spaMGCN, an innovative approach specifically designed for identifying spatial domains, especially in discrete tissue distributions. By integrating spatial transcriptomics and spatial epigenomic data through an autoencoder and a multi-scale adaptive graph convolutional network, spaMGCN outperforms baseline methods. Our evaluations demonstrate its effectiveness in recognizing discrete T cell zones in mouse spleens and follicular cells in human lymph nodes, as well as effectively distinguishing capsule structures from surrounding tissues.

The rapid advancements in spatially resolved transcriptomics (SRT) enable the characterization of gene expressions while preserving spatial information. However, high dropout rates and noise hinder accurate spatial domain identification for understanding tissue architecture. We present DeepGFT, a method that simultaneously models spot-wise and gene-wise relationships by integrating deep learning with graph Fourier transform for spatial domain identification. Benchmarking results demonstrate the superiority of DeepGFT over existing methods. DeepGFT detects tumor substructures with immune-related differences in human breast cancer, identifies the complex germinal centers accurately in human lymph node, and accurately reveals the developmental changes in 3D Drosophila data.

This paper focuses on the problem of nonlinear system identification by proposing an improved approach for existing frequency-domain nonlinear identification through feedback of the outputs (NIFO) method via separation strategy. Suitable excitation level is difficult to select for the existing NIFO method, and coupling errors are usually caused by the large differences in the numerical magnitude between the excitation forces and the nonlinear description functions when both of them are simultaneously considered as an input vector. In this work, a nonlinear separation identification through feedback of the outputs (NSIFO) method is proposed to avoid the limitation of the selection range of the excitation level and reduce coupling errors of the existing NIFO method. The proposed method needs two excitation tests including the low-level excitation test and the high-level excitation test. The underlying linear frequency response function matrix is firstly identified under low-level excitation, and only nonlinear description functions are considered as an input to identify nonlinear parameters under high-level excitation by using separation strategy. Two numerical structural examples and a three-story experimental structure are, respectively, used to validate the effectiveness and feasibility of the proposed method via a comparative study focused on the identification of nonlinear systems. Numerical and experimental identification results finally demonstrate the superior achievable accuracy and stability of the proposed method compared to the existing NIFO method.

Purpose The purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method. Design/methodology/approach This method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel. Findings The algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight. Originality/value This paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

Spatially resolved transcriptomics (SRT) for characterizing spatial cellular heterogeneities in tissue environments requires systematic analytical approaches to elucidate gene expression variations within their physiological context. Here, we introduce SpaSEG, an unsupervised deep learning model utilizing convolutional neural networks for multiple SRT analysis tasks. Extensive evaluations across diverse SRT datasets generated by various platforms demonstrate SpaSEG’s superior robustness and efficiency compared to existing methods. In the application analysis of invasive ductal carcinoma, SpaSEG successfully unravels intratumoral heterogeneity and delivers insights into immunoregulatory mechanisms. These results highlight SpaSEG’s substantial potential for exploring tissue architectures and pathological biology.

Background Spatial transcriptomics now enables sequencing while preserving the spatial location of cells. This significantly enhances researchers' understanding of cellular and tissue functions in their spatial context. However, due to current technical limitations, spatial transcriptomics data often exhibit high dropout rates and noise, posing challenges for downstream analysis, like spot clustering, differential gene analysis, and spatial domain identification. To address those challenges, we propose stGRL, a novel deep multi-task graph neural network model tailored for spatial transcriptomics. stGRL employs an encoder-decoder architecture with a zero-inflated negative binomial (ZINB) distribution to reconstruct input data while effectively addressing dropout events. Additionally, it integrates graph contrastive representation learning to enhance the consistency of node embeddings, thereby improving clustering performance. Results Through benchmark experiments on various spatial transcriptomics datasets, stGRL demonstrated a superior ability to identify spatial features compared to current mainstream methods. In-depth analyses reveal that the denoised data generated by stGRL not only preserves the spatial hierarchy of tissues but also accurately identifies differentially expressed genes. When applied to breast cancer datasets, stGRL effectively analyzed the differences between cancerous regions and carcinoma in situ areas, uncovering that carcinoma in situ regions are predominantly regulated by the immune system, which limits cancer cell development through inflammatory responses. Additionally, in the spatial transcriptomics analysis of ovarian cancer, stGRL successfully annotated cell types, accurately identified B cell-enriched regions, and discovered a novel target gene, MZB1, with potential therapeutic value. Conclusions stGRL is an effective method for integrating multiple tasks in spatial transcriptome analysis. Our study highlights its broad applicability and outstanding performance in analyzing spatial transcriptome data. This method offers a powerful analytical tool for uncovering the spatial heterogeneity of complex tissues and identifying potential therapeutic targets for disease.

DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

In order to overcome the influence of complex environmental disturbance factors such as nonlinear time-varying characteristics on the dynamic control performance of small fixed-wing UAVs, the nonlinear expression relationship of neural networks (NNs) is combined with the recursive least squares (RLSs) identification algorithm. This paper proposes a hybrid aerodynamic parameter identification method based on NN-RLS offline network training and online learning correction. The simulation results show that compared with the real value of the identification value obtained by this algorithm, the residual error of the moment coefficient is reduced by 69%, and the residual error of the force coefficient is reduced by 89%. Under the same identification accuracy, the identification time is shortened from the original 0.1 s to 0.01 s. Compared with traditional identification algorithms, better estimation results can be obtained. By using this algorithm to continuously update the NN model and iterate repeatedly, iterative learning for complex dynamic models can be realized, providing support for the optimization of UAV control schemes.

This paper presents a novel approach to optimal input signal design for open-loop fractional-order system identification, using an integer-order approximation of the fractional operators to minimize the average input power. This is obtained by formulating the problem as an LMI (Linear Matrix Inequality) optimization problem with the limitation of achieving at least a specified model accuracy. The ORA (Oustaloup Recursive Approximation) method has been employed to model the fractional-order differentiation operator in discrete integer-order Output Error model form. The optimal input design is executed using finite-dimensional FIR (Finite Impulse Response) filter spectrum parameterization, where the decision variables are calculated through convex optimization. The A-optimality criterion has been used to examine the relationship between the input signal spectrum power and the accuracy of estimated models. Finally, numerical examples illustrate the proposed approach, confirming the method’s suitability for fractional-order system identification.

A series of shaking table tests was carried out on the dynamic performance and working mechanism of a gabion reinforced soil retaining wall under seismic load. The test results show that the panel presents the deformation mode of middle and upper bulging at the contact point between the rigid box and the retaining wall The settlement of top backfill is relatively uniform, and there is basically no differential settlement, the natural frequencies at different positions and heights inside the retaining wall are basically the same, and the natural frequencies are stable between 22.61 and 23.04 Hz below 0.8 g. The damping ratio decreases with the increase in wall height, and the damping ratio at each stage after vibration is greater than that before vibration. The seismic earth pressure is nonlinearly distributed. The measured value of the lower part of the retaining wall is smaller than that calculated by the Seed–Whitman method with an increase in peak acceleration, and the measured value of the upper part of the retaining wall is larger than the theoretical calculation results. The position of the resultant action point of seismic earth pressure is greater than 0.33 times the wall height specified by the Mononobe–Okabe method.