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With the rapid advancement of remote-sensing technology, the spectral information obtained from hyperspectral remote-sensing imagery has become increasingly rich, facilitating detailed spectral analysis of Earth’s surface objects. However, the abundance of spectral information presents certain challenges for data processing, such as the “curse of dimensionality” leading to the “Hughes phenomenon”, “strong correlation” due to high resolution, and “nonlinear characteristics” caused by varying surface reflectances. Consequently, dimensionality reduction of hyperspectral data emerges as a critical task. This paper begins by elucidating the principles and processes of hyperspectral image dimensionality reduction based on manifold theory and learning methods, in light of the nonlinear structures and features present in hyperspectral remote-sensing data, and formulates a dimensionality reduction process based on manifold learning. Subsequently, this study explores the capabilities of feature extraction and low-dimensional embedding for hyperspectral imagery using manifold learning approaches, including principal components analysis (PCA), multidimensional scaling (MDS), and linear discriminant analysis (LDA) for linear methods; and isometric mapping (Isomap), locally linear embedding (LLE), Laplacian eigenmaps (LE), Hessian locally linear embedding (HLLE), local tangent space alignment (LTSA), and maximum variance unfolding (MVU) for nonlinear methods, based on the Indian Pines hyperspectral dataset and Pavia University dataset. Furthermore, the paper investigates the optimal neighborhood computation time and overall algorithm runtime for feature extraction in hyperspectral imagery, varying by the choice of neighborhood k and intrinsic dimensionality d values across different manifold learning methods. Based on the outcomes of feature extraction, the study examines the classification experiments of various manifold learning methods, comparing and analyzing the variations in classification accuracy and Kappa coefficient with different selections of neighborhood k and intrinsic dimensionality d values. Building on this, the impact of selecting different bandwidths t for the Gaussian kernel in the LE method and different Lagrange multipliers λ for the MVU method on classification accuracy, given varying choices of neighborhood k and intrinsic dimensionality d, is explored. Through these experiments, the paper investigates the capability and effectiveness of different manifold learning methods in feature extraction and dimensionality reduction within hyperspectral imagery, as influenced by the selection of neighborhood k and intrinsic dimensionality d values, identifying the optimal neighborhood k and intrinsic dimensionality d value for each method. A comparison of classification accuracies reveals that the LTSA method yields superior classification results compared to other manifold learning approaches. The study demonstrates the advantages of manifold learning methods in processing hyperspectral image data, providing an experimental reference for subsequent research on hyperspectral image dimensionality reduction using manifold learning methods.

The direct removal of surface vegetation during surface coal mining has a negative impact on the surrounding ecological environment. Effective vegetation restoration is essential to mitigate these impacts. Therefore, accurate monitoring and assessment of vegetation restoration following mining disturbance is critical for ecological protection in mining areas. This study employs the Detecting Breakpoints and Estimating Segments in Trend (DBEST) to map the historical patterns of vegetation disturbance and subsequent recovery at the Shendong coal base. This is the first large-scale application of DBEST for such purposes. To examine the spatio-temporal trends in post-mining vegetation restoration, the Years to Recovery (Y2R) and amount of NDVI recovery were calculated based on the Normalized Difference Vegetation Index (NDVI) time-series. The results show that the DBEST has an accuracy of 0.90 in detecting vegetation destruction and 0.78 in detecting restoration. These findings highlight the substantial potential of this algorithm for monitoring vegetation disturbance in mining areas. The total area of vegetation destruction within the Shendong coal base is 449.65 km 2 , and the restoration area is 156.62 km 2 . Between 1992 and 2017, 46.90% of the disturbed areas achieved 80% of the pre-mining vegetation level, exceeding the average restoration level in China. The average Y2R was 4.68 years. Furthermore, NDVI restoration showed an initial increase followed by a decline with longer Y2R values, suggesting that while early restoration efforts were more effective, long-term restoration efficiency decreased. This finding emphasizes the necessity of concentrating on the restoration process at each stage of the planning and implementation of revegetation projects, particularly regarding the difficulties associated with long-term restoration. This is crucial for the development of more comprehensive and sustainable strategies.

Low temperatures are one of the major abiotic stresses that affect alfalfa’s development and yield. Enhancing frost resistance through resistance-related genes is one of the most effective ways to address this issue in alfalfa. Therefore, exploring cold-resistant gene resources and the cultivation of cold-resistant alfalfa cultivars is inevitable in order to achieve high yield and quality. In this study, we conducted transcriptome profiling of roots obtained from two alfalfa genotypes, i.e., Qingda No.1 for freeze tolerance and Gannong No.9 for freeze sensitivity. We observed that Qingda No.1 had more lateral roots and a more developed root system after overwintering, while Gannong No.9 had fewer lateral roots and an underdeveloped root system. After overwintering, Qingda No.1 exhibited higher superoxide dismutase (SOD) activity compared to Gannong No.9, while Gannong No.9 showed higher perosuperoxide dismutasexidase (POD) activity than Qingda No.1. We identified 25,935 differentially expressed genes, with 12 979 and 12 956 differential genes found in the freeze-tolerant variety Qingda No.1 group and the freeze-sensitive Gannong No.9 group, respectively. The enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways also differed between the two groups. We also discovered several gene family members, and the most frequent transcription factors were bHLH, B3, NAC, WRKY, and MYB_related. These findings provide comprehensive information to further understand the molecular mechanisms of adaptation to freezing stress in alfalfa and offer potential functional candidate genes for adaptation to abiotic stress.

Medicago sativa is the most widely cultivated forage legume and one of the most economically valuable crops throughout the world. Qingda no.1 (Medicago sativaL. cv. Qingda no.1) is an excellent alfalfa local variety with strong cold, drought and salt resistance in the three rivers source area of Qinghai. In this study, the whole chloroplast (cp) genome of Qingda no.1 was sequenced, assembled and its structure was analysed by the Illumina high-throughput sequencing technology. The results showed that the chloroplast genome of Qingda no.1 exhibits no obvious typical quadripartite structure; the total length of the chloroplast genome is 125 637 bp; the chloroplast genome contained 111 genes, including 77 protein-coding genes, 30 tRNA genes, and 4 rRNA genes, with an overall GC content of 38.33%. The relative synonymous codon usage showed that 68.67% of the codons RSCU > 1 in Qingda no.1, with the preference ending with A and T. The simple sequence repeat (SSR) analysis identified 62 SSR loci. The phylogenetic analysis of the cp genome, Qingda no.1 clustered closely with Medicago sativa KU321683 (Medicago sativaL. cv. KU321683). These results are helpful for the further study of the Qingda no.1 adaptation mechanism to high altitude stress environments.

To determine the relationship between maternal bone resorption and bone development in fetuses. Female SD rats were injected with either fluorescent calcium indicator calcein alone or together with tetracycline 1 week before pregnancy, followed by fluorescence detection in fetal tibias 21 days post-treatment. Alendronate was subsequently administered to pregnant rats to inhibit maternal bone resorption, while maternal bone turnover and fetal bone development were both examined. The maternal fluorescent labeled calcium before pregnancy was found in the fetal tibia. This indicated that the calcium of maternal bones may be released into the maternal circulation through high bone resorption during pregnancy, thereby participating in the fetal bone development. Bone histomorphometry and serum biomarker results showed that Alendronate significantly inhibited maternal bone resorption in pregnant rats when compared to normal pregnant rats. Moreover, the body weight, bone mass, and bone length of the fetuses in the Alendronate group were significantly decreased; while no apparent abnormality in placental morphology was observed. The above results implied that when maternal bone resorption is suppressed, the development of the fetal bone shall also be suppressed. Calcium in the maternal bone is released into the maternal circulation through bone resorption during pregnancy which represents an important material source in fetal bone development. Therefore, high bone turnover during pregnancy is essential for mammalian embryonic bone development.

This study aims to develop a fine-grained transmission line ice-cover prediction model and conduct an optimization study. Ice cover is one of the important factors leading to transmission line accidents, so accurately predicting the ice cover of transmission lines is crucial to ensure the safe operation of power grids. In this paper, an efficient ice-cover prediction model is constructed based on advanced machine learning algorithms and optimization methods and is validated and optimized with a large amount of experimental data. The results show that the proposed model has high accuracy and prediction capability in ice cover prediction, which provides important support for the safe operation of transmission lines.

Overstocked dumping sites associated with open-pit coal mining occupy original vegetation areas and cause damage to the environment. The monitoring of vegetation disturbance and restoration at dumping sites is important for the accurate planning of ecological restoration in mining areas. This paper aimed to monitor and assess vegetation disturbance and restoration in the dumping sites of the Baorixile open-pit mine using the LandTrendr algorithm and remote sensing images. Firstly, based on the temporal datasets of Landsat from 1990 to 2021, the boundaries of the dumping sites in the Baorixile open-pit mine in Hulunbuir city were extracted. Secondly, the LandTrendr algorithm was used to identify the initial time and duration of vegetation disturbance and restoration, while the Normalized Difference Vegetation Index (NDVI) was used as the input parameter for the LandTrendr algorithm. Thirdly, the vegetation restoration effect at the dumping sites was monitored and analyzed from both temporal and spatial perspectives. The results showed that the dumping sites of the Baorixile open-pit mine were disturbed sharply by the mining activities. The North dumping site, the South dumping site, and the East dumping site (hereinafter referred to as the North site, the South site, and the East site) were established in 1999, 2006, and 2010, respectively. The restored areas were mainly concentrated in the South site, the East site, and the northwest of the North site. The average restoration intensity in the North site, South site, and East site was 0.515, 0.489, and 0.451, respectively, and the average disturbance intensity was 0.371, 0.398, and 0.320, respectively. The average restoration intensity in the three dumping sites was greater than the average disturbance intensity. This study demonstrates that the combination of temporal remote sensing images and the LandTrendr algorithm can follow the vegetation restoration process of an open-pit mine clearly and can be used to monitor the progress and quality of ecological restoration projects such as vegetation restoration in mining areas. It provides important data and support for accurate ecological restoration in mining areas.

Aiming at the current problem of lack of objective prediction products for deviation prediction along transmission lines, a deviation prediction model is established based on the historical statistical characteristics of deviation of transmission lines by using European fine-grid numerical prediction products and actual observation data. The model is combined with the operation specification of transmission lines, and the European fine-grid numerical prediction product is revised by using the normalization method to obtain a more accurate deviation prediction product. By analyzing the differences between the European fine-grid numerical prediction product and the actual observation data, the deviation prediction software is established, which can effectively improve the prediction accuracy and stability of the deviation situation of transmission lines. The results show that the software has good prediction performance and can effectively reduce the false alarm rate and omission rate of transmission line deviation prediction and improve the reliability and stability of the power grid.

Activation of osteoblasts in bone formation and osteoclasts in bone resorption is important during the bone fracture healing process. There has been a long interest in identifying and developing a natural therapy for bone fracture healing. In this study, we investigated the regulation of osteoclast differentiation by baicalin, which is a natural molecule extracted from Eucommiaulmoides (small tree native to China). It was determined that baicalin enhanced osteoclast maturation and bone resorption activity in a dose‐dependent manner. Moreover, this involves the activation of MAPK, increased Mitf nuclear translocation and up‐regulation of downstream osteoclast‐related target genes expression. The baicalin‐induced effect on osteoclast differentiation can be mimicked by specific inhibitors of p‐ERK (U0126) and the Mitf‐specific siRNA, respectively. Protein–ligand docking prediction identified that baicalin might bind to RANK, which is the upstream receptor of p‐ERK/Mitf signalling in osteoclasts. This indicated that RANK might be the binding target of baicalin. In sum, our findings revealed baicalin increased osteoclast maturation and function via p‐ERK/Mitf signalling. In addition, the results suggest that baicalin can potentially be used as a natural product for the treatment of bone fracture.

Laparoscopic surgery for malignant solid tumors is still in the stage of clinical exploration. Neuroblastoma is a common solid tumor in children. The present study discussed significance and feasibility of complete resection of stage III neuroblastoma by laparoscopic surgery and its safety and effectiveness was compared with traditional surgery. For children suffering from neuroblastoma with large tumor volume and vascular invasion, preoperative chemotherapy can be given and minimally invasive laparoscopic surgery can be one option to be considered when the tumor volume is <6 cm. During the operation, the tumor tissue can be removed by segmental resection and the removal of as much tumor tissue as possible is an important factor in improving the prognosis. Laparoscopic minimally invasive surgery is associated with minimal surgical trauma and quick recovery of patients, and children can receive postoperative chemotherapy as early as possible, which is conducive to good recovery. Basically, the prerequisite and requirements for performing this operation are professional laparoscopic skills and an experienced team.