Towards large-scale single-shot millimeter-wave imaging for low-cost security inspection.

Millimeter-Wave (MMW) imaging is a promising technique for contactless security inspection. However, the high cost of requisite large-scale antenna arrays hinders its widespread application in high-throughput scenarios. Here, we report a large-scale single-shot MMW imaging framework, achieving low-cost high-fidelity security inspection. We first analyzed the statistical ranking of each array element through 1934 full-sampled MMW echoes. The highest-ranked elements are preferentially selected based on the ranking, building the experimentally optimal sparse sampling strategy that reduces antenna array cost by one order of magnitude. Additionally, we derived an untrained interpretable learning scheme, realizing robust and accurate MMW image reconstruction from sparsely sampled echoes. Last, we developed a neural network for automatic object detection, and experimentally demonstrated successful detection of concealed centimeter-sized targets using 10% sparse array, whereas all the other contemporary approaches failed at such a low sampling ratio. With the strong detection ability and order-of-magnitude cost reduction, we anticipate that this technique provides a practical way for large-scale single-shot MMW imaging.

Meta-Attention Network Based Spectral Reconstruction with Snapshot Near-Infrared Metasurface.

Near-infrared (NIR) spectral information is important for detecting and analyzing material compositions. However, snapshot NIR spectral imaging systems still pose significant challenges owing to the lack of high-performance NIR filters and bulky setups, preventing effective encoding and integration with mobile devices. This study introduces a snapshot spectral imaging system that employs a compact NIR metasurface featuring 25 distinct C4 symmetry structures. Benefitting from the sufficient spectral variety and low correlation coefficient among these structures, center-wavelength accuracy of 0.05 nm and full width at half maximum accuracy of 0.13 nm are realized. The system maintains good performance within an incident angle of 1°. A novel meta-attention network prior iterative denoising reconstruction (MAN-IDR) algorithm is developed to achieve high-quality NIR spectral imaging. By leveraging the designed metasurface and MAN-IDR, the NIR spectral images, exhibiting precise textures, minimal artifacts in the spatial dimension, and little crosstalk between spectral channels, are reconstructed from a single grayscale recording image. The proposed NIR metasurface and MAN-IDR hold great promise for further integration with smartphones and drones, guaranteeing the adoption of NIR spectral imaging in real-world scenarios such as aerospace, health diagnostics, and machine vision.

Intracerebral hemorrhage-induced brain injury in mice: The role of peroxiredoxin 2-Toll-like receptor 4 inflammatory axis.

BACKGROUND: Peroxiredoxin 2 (Prx2), an intracellular protein that regulates redox reactions, released from red blood cells is involved in inflammatory brain injury after intracerebral hemorrhage (ICH). Toll-like receptor 4 (TLR4) may be crucial in this process. This study investigated the role of the Prx2-TLR4 inflammatory axis in brain injury following experimental ICH in mice. METHODS: First, C57BL/6 mice received an intracaudate injection of autologous arterial blood or saline and their brains were harvested on day 1 to measure Prx2 levels. Second, mice received an intracaudate injection of either recombinant mouse Prx2 or saline. Third, the mice were co-injected with autologous arterial blood and conoidin A, a Prx2 inhibitor, or vehicle. Fourth, the mice received a Prx2 injection and were treated with TAK-242, a TLR4 antagonist, or saline (intraperitoneally). Behavioral tests, magnetic resonance imaging, western blot, immunohistochemistry/immunofluorescence staining, and RNA sequencing (RNA-seq) were performed. RESULTS: Brain Prx2 levels were elevated after autologous arterial blood injection. Intracaudate injection of Prx2 caused brain swelling, microglial activation, neutrophil infiltration, neuronal death, and neurological deficits. Co-injection of conoidin A attenuated autologous arterial blood-induced brain injury. TLR4 was expressed on the surface of microglia/macrophages and neutrophils and participated in Prx2-induced inflammation. TAK-242 treatment attenuated Prx2-induced inflammation and neurological deficits. CONCLUSIONS: Prx2 can cause brain injury following ICH through the TLR4 pathway, revealing the Prx2-TLR4 inflammatory axis as a potential therapeutic target.

Global-optimal semi-supervised learning for single-pixel image-free sensing.

Single-pixel sensing offers low-cost detection and reliable perception, and the image-free sensing technique enhances its efficiency by extracting high-level features directly from compressed measurements. However, the conventional methods have great limitations in practical applications, due to their high dependence on large labelled data sources and incapability to do complex tasks. In this Letter, we report an image-free semi-supervised sensing framework based on GAN and achieve an end-to-end global optimization on the part-labelled datasets. Simulation on the MNIST realizes 94.91% sensing accuracy at 0.1 sampling ratio, with merely 0.3% of the dataset holding its classification label. When comparing to the conventional single-pixel sensing methods, the reported technique not only contributes to a high-robust result in both conventional (98.49% vs. 97.36%) and resource-constrained situations (94.91% vs. 83.83%) but also offers a more practical and powerful detection fashion for single-pixel sensing, with much less human effort and computation resources.