Accurately reconstructing a three-dimensional (3D) ocean sound speed field (SSF) is essential for various ocean acoustic applications, but the sparsity and uncertainty of sound speed samples across a vast ocean region make it a challenging task. To tackle this challenge, a large body of reconstruction methods has been developed, including spline interpolation, matrix/tensor-based completion, and deep neural networks (DNNs)-based reconstruction. However, a principled analysis of their effectiveness in 3D SSF reconstruction is still lacking. This paper performs a thorough analysis of the reconstruction error and highlights the need for a balanced representation model that integrates expressiveness and conciseness. To meet this requirement, a 3D SSF-tailored tensor DNN is proposed, which uses tensor computations and DNN architectures to achieve remarkable 3D SSF reconstruction. The proposed model not only includes the previous tensor-based SSF representation model as a special case but also has a natural ability to reject noise. The numerical results using the South China Sea 3D SSF data demonstrate that the proposed method outperforms state-of-the-art methods. The code is available at https://github.com/OceanSTARLab/Tensor-Neural-Network.

Background: Interleukin-35 (IL-35), a novel immune-suppressing cytokine, can promote tumor angiogenesis and inhibits anti-tumor cytotoxic lymphocyte response. Here, we aimed to investigate the potential mechanism of the effects of IL-35 on anti-tumor cytotoxic lymphocyte. Methods: Dendritic cells (DCs) were used to induce anti-tumor cytotoxic lymphocyte. Flow cytometry, carboxyfluorescein succinimidyl ester staining, ELISA assay and western blotting were used to analyze the effect of IL-35 on anti-tumor cytotoxic lymphocyte. Results: We observed that IL-35 inhibited the expression of costimulatory molecule CD28 on CD8⁺ T cell surface and Th1 cytokine production. However, IL-35 did not inhibit anti-tumor cytotoxic lymphocyte proliferation nor enhance the expression of apoptosis-related proteins of anti-tumor cytotoxic lymphocyte. Moreover, IL-35 did not repress the expression of Fas ligand (FasL) on cytotoxic lymphocyte surface. Conclusions: Our findings revealed that IL-35 can inhibit CD8⁺ T cells activity by suppressing the expression of costimulatory molecule CD28 and Th1 cytokine production.

In this study, a bound-constrained optimization algorithm is applied for estimating physiological data (pulse and breathing rate) of human body using 60 GHz Doppler radar, by detecting displacements induced by breathing and the heartbeat of a human subject. The influence of mutual phasing between the two movements is analyzed in a theoretical framework and the application of optimization algorithms is proved to be able to accurately detect both breathing and heartbeat rates, despite intermodulation effects between them. Different optimization procedures are compared and shown to be more robust to receiver noise and artifacts of random body motion than a direct spectrum analysis. In case of a large-scale constrained bound, a parallel optimization procedure executed in subranges is proposed to realize accurate detection in a reduced span of time.

Metasurface holography offers significant advantages, including a broad field of view, minimal noise, and high imaging quality, making it valuable across various optical domains such as 3D displays, VR, and color displays. However, most passive pure-structured metasurface holographic devices face a limitation: once fabricated, as their functionality remains fixed. In recent developments, the introduction of multiplexed and reconfigurable metasurfaces breaks this limitation. Here, the comprehensive progress in holography from single metasurfaces to multiplexed and reconfigurable metasurfaces is reviewed. First, single metasurface holography is briefly introduced. Second, the latest progress in angular momentum multiplexed metasurface holography, including basic characteristics, design strategies, and diverse applications, is discussed. Next, a detailed overview of wavelength-sensitive, angle-sensitive, and polarization-controlled holograms is considered. The recent progress in reconfigurable metasurface holography based on lumped elements is highlighted. Its instant on-site programmability combined with machine learning provides the possibility of realizing movie-like dynamic holographic displays. Finally, we briefly summarize this rapidly growing area of research, proposing future directions and potential applications.

In this tutorial, we introduce a solver of monochromatic Maxwell equations made freely available at https://www.fresnel.fr/perso/chaumet/ifdda.html, based on the volume moment method. The Institut Fresnel Discrete Dipole Approximation or Idiot-Friendly Discrete Dipole Approximation (IFDDA) calculates the diffracted field, the optical forces, and the image through a microscope of any three-dimensional inhomogeneous object, possibly anisotropic, placed in a stratified medium. In this method, only the object is meshed so the required memory space is kept to a minimum. We describe the principle and the potentialities of IFDDA and present comparisons with Mie theory and experimental data to assess the accuracy of the method. In addition, we provide a user guide for first steps with the solver. We hope that you will use and enjoy this numerical tool!

Reconstructing ocean sound speed field (SSF) from limited and noisy measurements/estimates is crucial for many ocean acoustic applications, including underwater tomography, target localization/tracking, and communications. Classical reconstruction methods include deterministic approaches (e.g., spline interpolation) and geostatistical methods (e.g., kriging). They exhibit a strong link to linear regression and Gaussian process regression in machine learning (ML) literature, by uniformly viewing them as supervised regression models that learn the mapping from the geographical locations to the sound speed outputs. From a unified ML perspective, theoretical analysis indicates that classical reconstruction methods have several drawbacks, such as the sensitivity to noises and high computational cost. To overcome these drawbacks, inspired by the recent thriving development of graph machine learning, we introduce graph-guided Bayesian low-rank matrix completions (LRMCs) for fine-scale and accurate ocean SSF reconstruction. In particular, a more general graph-guided LRMC model is proposed that encompasses the state-of-the-art one as a special case. The proposed model and the associated inference algorithm simultaneously exploit the global (low-rankness) and local (graph structure) information of ocean sound speed data, thus striking an outstanding balance of reconstruction accuracy and computational complexity. Numerical results using real-life ocean SSF data have demonstrated the encouraging performances of the proposed approaches.