Insufficient bone matrix formation caused by diabetic chronic inflammation can result in bone nonunion, which is perceived as a worldwide epidemic, with a substantial socioeconomic and public health burden. Macrophages in microenvironment orchestrate the inflammation and launch the process of bone remodeling and repair, but aberrant activation of macrophages can drive drastic inflammatory responses during diabetic bone regeneration. In diabetes mellitus, the proliferation of resident macrophages in bone microenvironment is limited, while enhanced myeloid differentiation of hematopoietic stem cells (HSCs) leads to increased and constant monocyte recruitment and thus macrophages shift toward the classic pro-inflammatory phenotype, which leads to the deficiency of bone regeneration. In this review, we systematically summarized the anomalous origin of macrophages under diabetic conditions. Moreover, we evaluated the deficit of pro-regeneration macrophages in the diabetic inflammatory microenvironment. Finally, we further discussed the latest developments on strategies based on targeting macrophages to promote diabetic bone regeneration. Briefly, this review aimed to provide a basis for modulating the biological functions of macrophages to accelerate bone regeneration and rescue diabetic fracture healing in the future.

In-air handwriting necessitates consistent motion tracking, in contrast to millimeter-wave (mmWave) radar-based simple gesture recognition techniques. However, during long-duration gesture tracking, challenges, such as body motion interference and environmental clutter, become more pressing. Moreover, due to the lack of a supporting surface in in-air handwriting, slight arm tremors also can result in unsmooth trajectories. To address these challenges, this article proposes a two-stage processing framework called MMHTSR. In the first stage, the state-space equations are reestablished, and a locally correlated 2-D Gaussian process regression (GPR) algorithm is employed for interframe prediction. By incorporating uncertainty estimation, weights are assigned to the next frame data, effectively suppressing interference from nongestural targets. In the second stage, real-time smoothing and tracking of gesture trajectories are accomplished using a Kalman filter, followed by mapping the trajectories onto the Cartesian coordinate system. Finally, an end-to-end signal processing framework is deployed on a low-cost 60-GHz mmWave radar prototype, and gesture trajectory recognition is achieved using deep learning methods. Experimental results demonstrate that MMHTSR can accurately track motion gestures within the range of approximately 5–40 cm and successfully recognize 30 classes of in-air gesture trajectories, including uppercase letters A–Z and four interactive gesture actions. Furthermore, the proposed framework exhibits robust performance across various scenarios which shows its adaptability.

The features employed for SAR target recognition have evolved from traditional SAR target geometric features and pattern features to modern deep features, indicating a trend of increasing recognition accuracy but decreasing feature interpretability. Therefore, the fusion of multi-dimensional features has been investigated by many researchers. Existing feature fusion methods typically involve simple concatenation or addition of geometric features and pattern features with deep features, or directly incorporating them into deep networks. However, such fusion methods mentioned above inadequately consider the potential conflicts between features and hard to fully exploit multi-dimensional features. To solve above problem, a multi-dimensional feature joint learning framework (FJL-Framework) that serves the SAR target recognition task is proposed in this paper, which consists of three models. Specifically, the SGC-GA-Model can select pattern features for SAR targets based on geometric feature constraints, the GFIC-Model can select deep features with high-level abstract semantics, and the MFFS-Model can complement and fuse these two types of features to maximize the utilization of feature information. Experiments and comprehensive ablation studies on four datasets, namely OpenSARShip-1.0, FUSAR-Ship, MSTAR-T72Variants, and SAR-AIRcraft-1.0, collectively demonstrate that the recognition performance of our proposed FJL-Framework outperforms the current state-of-the-art methods.

Multi-scale target detection in synthetic aperture radar (SAR) images is one of the key techniques of SAR image interpretation, which is widely used in national defense and security. However, multi-scale targets include several types. For example, targets with similar-scale, large-scale, and ultra-large-scale differences coexist in SAR images. In particular, it is difficult for existing target detection methods to detect both ultra-large-scale targets and ultra-small-scale targets in SAR images, resulting in poor detection results for these two types of targets. To solve these problems, this paper proposes an ultra-high precision deep learning network (UltraHi-PrNet) to detect dense multi-scale targets. Firstly, a novel scale transfer layer is constructed to transfer the features of targets of different scales from bottom networks to top networks, ensuring that the features of ultra-small-scale, small-scale, and medium-scale targets in SAR images can be extracted more easily. Then, a novel scale expansion layer is constructed to increase the range of the receptive field of feature extraction without increasing the feature resolution, ensuring that the features of large-scale and ultra-large-scale targets in SAR images can be extracted more easily. Next, the scale expansion layers with different expansion rates are densely connected to different stages of the backbone network, and the features of the target with ultra-large-scale differences are extracted. Finally, the classification and regression of targets were achieved based on Faster R-CNN. Based on the SAR ship detection dataset (SSDD), AIR-SARShip-1.0, high-resolution SAR ship detection dataset-2.0 (high-resolution SSDD-2.0), the SAR-ship-dataset, and the Gaofen-3 airport dataset, the experimental results showed that this method can detect similar-scale, large-scale, and ultra-large-scale targets more easily. At the same time, compared with other advanced SAR target detection methods, the proposed method can achieve higher accuracy.

Synthetic Aperture Radar (SAR) targets often exhibit characteristics such as high mobility and strong concealment, resulting in scarce SAR data and the manifestation of few-shot data properties. These few-shot SAR targets are susceptible to interference from complex background information and mutual interference of target features, making it challenging to distinguish SAR targets from the background. Additionally, there is confusion in features among different targets, leading to models being highly insensitive to few-shot SAR targets under complex distribution conditions in new tasks. Similarly, these few-shot SAR targets exhibit significant sample scarcity and sample variations, resulting in pronounced fluctuations in class centers and difficulty in determining sample distributions. This leads to challenges in accurately representing the potential representative features of few-shot SAR targets by the model. To address these issues, further enhancement of SAR target features is necessary to provide a robust foundation for the ultimate aggregation module. Therefore, based on the meta-learning paradigm, we propose a method for few-shot target detection in SAR imagery via intensive meta-feature aggregation (IMFA), aiming to reinforce SAR target features for improved representation. Specifically, firstly, we propose a novel hierarchical multi-head cross attention (HMCA) to capture global multiscale contextual information in different subspaces and analyze representative features between different targets to distinguish SAR targets from the background. Then, based on HMCA, we introduce a novel feature coupling module (FCM) to couple support features with cognitive information from the query image on the support branch. This is done to reduce the confusion and mutual interference of features between targets while enhancing the model’s generalization ability on new tasks. Finally, on the support branch with query-aware information, we construct a Gaussian distribution to estimate the class distribution of few-shot SAR targets and replace traditional class prototypes. On this basis, we propose the feature information maximization module (FIMM) to avoid feature information shift, greatly strengthening the expression of potential features. Through these steps, reinforced meta-features can be obtained, enabling efficient aggregation. Experiments on the SRSDD-v1.0 and MSAR-1.0 datasets demonstrate that our method has consistently outperformed state-of-the-art approaches in all configurations, achieving state-of-the-art performance.