Compared with non-degradable materials, biodegradable biomaterials play an increasingly important role in the repairing of severe bone defects, and have attracted extensive attention from researchers. In the treatment of bone defects, scaffolds made of biodegradable materials can provide a crawling bridge for new bone tissue in the gap and a platform for cells and growth factors to play a physiological role, which will eventually be degraded and absorbed in the body and be replaced by the new bone tissue. Traditional biodegradable materials include polymers, ceramics and metals, which have been used in bone defect repairing for many years. Although these materials have more or fewer shortcomings, they are still the cornerstone of our development of a new generation of degradable materials. With the rapid development of modern science and technology, in the twenty-first century, more and more kinds of new biodegradable materials emerge in endlessly, such as new intelligent micro-nano materials and cell-based products. At the same time, there are many new fabrication technologies of improving biodegradable materials, such as modular fabrication, 3D and 4D printing, interface reinforcement and nanotechnology. This review will introduce various kinds of biodegradable materials commonly used in bone defect repairing, especially the newly emerging materials and their fabrication technology in recent years, and look forward to the future research direction, hoping to provide researchers in the field with some inspiration and reference.

This paper introduces an approach for image shadow removal by using pulse coupled neural network (PCNN), based on the phenomena of synchronous pulse bursts in the animal visual cortexes. Two shadow-removing criteria are proposed. These two criteria decide how to choose the optimal parameter (the linking strength beta). The computer simulation results of shadow removal based on PCNN show that if these two criteria are satisfied, shadows are removed completely and the shadow-removed images are almost as the same as the original nonshadowed images. The shadow removal results are independent of changes of intensities of shadows in some range and variations of the places of shadows. When the first criterion is satisfied, even if the second criterion is not satisfied, as to natural grey images that have abundant grey levels, shadows also can be removed and PCNN shadow-removed images retain the shapes of the objects in original images. These two criteria also can be used for color images by dividing a color image into three channels (R, G, B). For shadows varying drastically, such as the noisy points in images, these two criteria are still right, but difficult to satisfy. Therefore, this approach can efficiently remove shadows that do not include the random noise.

Effective feature representations play a decisive role in content-based remote sensing image retrieval (CBRSIR). Recently, learning-based features have been widely used in CBRSIR and they show powerful ability of feature representations. In addition, a significant effort has been made to improve learning-based features from the perspective of the network structure. However, these learning-based features are not sufficiently discriminative for CBRSIR. In this paper, we propose two effective schemes for generating discriminative features for CBRSIR. In the first scheme, the attention mechanism and a new attention module are introduced to the Convolutional Neural Networks (CNNs) structure, causing more attention towards salient features, and the suppression of other features. In the second scheme, a multi-task learning network structure is proposed, to force learning-based features to be more discriminative, with inter-class dispersion and intra-class compaction, through penalizing the distances between the feature representations and their corresponding class centers. Then, a new method for constructing more challenging datasets is first used for remote sensing image retrieval, to better validate our schemes. Extensive experiments on challenging datasets are conducted to evaluate the effectiveness of our two schemes, and the comparison of the results demonstrate that our proposed schemes, especially the fusion of the two schemes, can improve the baseline methods by a significant margin.

Previous studies on the mechanisms of peripheral nerve injury (PNI) have mainly focused on the pathophysiological changes within a single injury site. However, recent studies have indicated that within the central nervous system, PNI can lead to changes in both injury sites and target organs at the cellular and molecular levels. Therefore, the basic mechanisms of PNI have not been comprehensively understood. Although electrical stimulation was found to promote axonal regeneration and functional rehabilitation after PNI, as well as to alleviate neuropathic pain, the specific mechanisms of successful PNI treatment are unclear. We summarize and discuss the basic mechanisms of PNI and of treatment via electrical stimulation. After PNI, activity in the central nervous system (spinal cord) is altered, which can limit regeneration of the damaged nerve. For example, cell apoptosis and synaptic stripping in the anterior horn of the spinal cord can reduce the speed of nerve regeneration. The pathological changes in the posterior horn of the spinal cord can modulate sensory abnormalities after PNI. This can be observed in cases of ectopic discharge of the dorsal root ganglion leading to increased pain signal transmission. The injured site of the peripheral nerve is also an important factor affecting post-PNI repair. After PNI, the proximal end of the injured site sends out axial buds to innervate both the skin and muscle at the injury site. A slow speed of axon regeneration leads to low nerve regeneration. Therefore, it can take a long time for the proximal nerve to reinnervate the skin and muscle at the injured site. From the perspective of target organs, long-term denervation can cause atrophy of the corresponding skeletal muscle, which leads to abnormal sensory perception and hyperalgesia, and finally, the loss of target organ function. The mechanisms underlying the use of electrical stimulation to treat PNI include the inhibition of synaptic stripping, addressing the excessive excitability of the dorsal root ganglion, alleviating neuropathic pain, improving neurological function, and accelerating nerve regeneration. Electrical stimulation of target organs can reduce the atrophy of denervated skeletal muscle and promote the recovery of sensory function. Findings from the included studies confirm that after PNI, a series of physiological and pathological changes occur in the spinal cord, injury site, and target organs, leading to dysfunction. Electrical stimulation may address the pathophysiological changes mentioned above, thus promoting nerve regeneration and ameliorating dysfunction.