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Dental hard tissues lack the ability to self-heal. In dentin and cementum, hydroxyapatite (HA) can exist outside and/or inside collagen fibers. It is difficult to repair or regenerate HA with a highly ordered orientation in the presence of collagen fibers. At present, the biomimetic mineralization of dentin and cementum, mainly carried out by imitating its biological formation process and its physiological structure, can be divided into those originating from the fiber mineralization mechanism and those with HA as the main component. The materials used include natural materials such as demineralized dentin matrix (DDM) and calcined bovine hydroxyapatite (BHA), and synthetic materials such as polymer-induced liquid precursor (PILP) and synthetic HA. In the future, natural materials and synthetic materials should be combined for the restoration and regeneration of dentin and cementum by means of biomimetic mineralization of calcium phosphate released by remineralization solution-HA.

To create a deep learning (DL) model that can accurately detect and classify three distinct types of rat dorsal root ganglion neurons: normal, segmental chromatolysis, and central chromatolysis. The DL model has the potential to improve the efficiency and precision of neuron classification in research related to spinal injuries and diseases. Methods: H&E slide images were divided into an internal training set (80%) and a test set (20%). The training dataset was labeled by two pathologists using pre-defined grades. Using this dataset, a two-component DL model was developed with the first component being a convolutional neural network (CNN) that was trained to detect the region of interest (ROI) and the second component being another CNN used for classification. Results: A total of 240 lumbar dorsal root ganglion (DRG) pathology slide images from rats were analyzed. The internal testing results showed an accuracy of 93.13%, and the external dataset testing demonstrated an accuracy of 93.44%. Conclusion: The DL model demonstrated a level of agreement comparable to that of pathologists in detecting and classifying normal and segmental chromatolysis neurons, although its agreement was slightly lower for central chromatolysis neurons. Significance: DL in improving the accuracy and efficiency of pathological analysis suggests that it may have a role in enhancing medical decision-making. [ABSTRACT FROM AUTHOR]

Granulite facies rocks provide important keys to evaluating collisional metamorphism in orogenic belts. The mafic granulites of Baoding in the Fuping Complex of the North China Craton occur within the Trans-North China Orogen (TNCO), a major Paleoproterozoic collisional orogen. Here, we present results from detailed investigations on newly discovered garnet pyroxenite, garnet two-pyroxene granulite, and garnet-bearing-plagioclase amphibolite using petrographic, mineralogical, geochemical, and zircon U-Pb dating methods. Our results show that the Fuping Complex metamorphic evolution in this study evolved in four stages: prograde (M1), high-pressure granulite facies (M2), granulite facies (M3), and retrograde (M4) stages. The mineral assemblage of the prograde stage (M1) consists of Amp + Pl + Q within garnet cores. The mineral assemblage of high-pressure granulite facies at the peak stage (M2) consists of Gt + Cpx + Pl + Q ± Amp, forming the garnet pyroxenite. The granulite facies stage M3 is characterized by the occurrence of orthopyroxene, with a mineral assemblage of Gt + Cpx + Opx + Amp+ Pl + Q. The early retrograde stage M4-1 includes clinopyroxenes scattered inside amphiboles, following the breakdown of garnet and clinopyroxene. The mineral assemblage of this stage comprises Amp + Pl + Q + Ilm ± Cpx. Later, in the late retrograde stage M4-2, the composition of amphiboles changed to actinolite, and epidote and chlorite started to appear in the matrix. Traditional geothermobarometry yielded P-T conditions of 700~706 °C and 6.0~6.2 kbar for prograde stage M1, 854~920 °C and 13.0~13.8 kbar for high-pressure granulite facies stage M2, 912~939 °C and 8.1~9.9 kbar for M3, 661~784 °C and 3.1~4.4 kbar for M4-1, and 637~638 °C, 1.1~1.3 kbar for M4-2, along a clockwise P-T path with a nearly isothermal decompression (ITD) and slight heating. Zircon LA-ICP-MS U-Pb dating constrains the timing of the high-pressure granulite facies metamorphic event to be between 1.83 and 1.86 Ga. Geochemical features suggest that the protoliths of the high-pressure granulites may have formed in an island arc environment within a convergent margin setting. Together with results from previous studies, our data suggest that the ~1.85 Ga metamorphic age recorded in the Fuping Complex represents a regional metamorphism in the TNCO, associated with the subduction–collision and assembly of the Eastern and Western Blocks of the NCC. [ABSTRACT FROM AUTHOR]

The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The detection of infrared dim and small targets in complex backgrounds is very challenging because of the low signal-to-noise ratio of targets and the drastic change in background. Low-rank sparse decomposition based on the structural characteristics of infrared images has attracted the attention of many scholars because of its good interpretability. In order to improve the sensitivity of sliding window size, insufficient utilization of time series information, and inaccurate tensor rank in existing methods, a four-dimensional tensor model based on superpixel segmentation and statistical clustering is proposed for infrared dim and small target detection (ISTD). First, the idea of superpixel segmentation is proposed to eliminate the dependence of the algorithm on selection of the sliding window size. Second, based on the improved structure tensor theory, the image pixels are statistically clustered into three types: corner region, flat region, and edge region, and are assigned different weights to reduce the influence of background edges. Next, in order to better use spatiotemporal correlation, a Four-Dimensional Fully-Connected Tensor Network (4D-FCTN) model is proposed in which 3D patches with the same feature types are rearranged into the same group to form a four-dimensional tensor. Finally, the FCTN decomposition method is used to decompose the clustered tensor into low-dimensional tensors, with the alternating direction multiplier method (ADMM) used to decompose the low-rank background part and sparse target part. We validated our model across various datasets, employing cutting-edge methodologies to assess its effectiveness in terms of detection precision and reduction of background interference. A comparative analysis corroborated the superiority of our proposed approach over prevailing techniques. [ABSTRACT FROM AUTHOR]

Due to their devastating ability to extract features, convolutional neural network (CNN)-based approaches have achieved tremendous success in hyperspectral image (HSI) classification. However, previous works have been dedicated to constructing deeper or wider deep learning networks to obtain exceptional classification performance, but as the layers get deeper, the gradient disappearance problem impedes the convergence stability of network models. Additionally, previous works usually focused on utilizing fixed-scale convolutional kernels or multiple available, receptive fields with varying scales to capture features, which leads to the underutilization of information and is vulnerable to feature learning. To remedy the above issues, we propose an innovative hybrid-scale feature enhancement network (HFENet) for HSI classification. Specifically, HFENet contains two key modules: a hybrid-scale feature extraction block (HFEB) and a shuffle attention enhancement block (SAEB). HFEB is designed to excavate spectral–spatial structure information of distinct scales, types, and branches, which can augment the multiplicity of spectral–spatial features while modeling the global long-range dependencies of spectral–spatial informative features. SAEB is devised to adaptively recalibrate spectral-wise and spatial-wise feature responses to generate the purified spectral–spatial information, which effectively filters redundant information and noisy pixels and is conducive to enhancing classification performance. Compared with several sophisticated baselines, a series of experiments conducted on three public hyperspectral datasets showed that the accuracies of OA, AA, and Kappa all exceed 99%, demonstrating that the presented HFENet achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic feature network (DSMSFNet) for HSI classification is proposed. This model is composed of a decompressed spectral-spatial feature extraction module (DSFEM) and a multiscale semantic feature extraction module (MSFEM). The former is devised to extract more discriminative and representative global decompressed spectral-spatial features in a lightweight extraction manner, while the latter is constructed to expand the range of available receptive fields and generate clean multiscale semantic features at a granular level to further enhance the classification performance. Compared with progressive classification approaches, abundant experimental results on three benchmark datasets prove the superiority of our developed DSMSFNet model. [ABSTRACT FROM AUTHOR]

The problem of the decomposition of an exponential sum sequence is a classical mathematical problem. Prony method and Prony-Kung method are the classical methods to solve this problem. The Prony-Kung method was analyzed in detail from the point of view of support subspace. On this basis, it was proved theoretically that the problem of the decomposition of an exponential sum sequence could be reduced to the determination of the support subspace, and a Lloyd-like algorithm with low-complexity for searching the support subspace for contaminated exponential sum sequence by noised was proposed. Finally, the above algorithm was applied to estimate millimeter wave channel, and the simulation results show the effectiveness of this application. [ABSTRACT FROM AUTHOR]