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Ferroptosis is a unique mode of cell death driven by iron-dependent lipid peroxidation, and the process is regulated by a variety of cellular metabolic pathways, including redox homeostasis, iron processing, and lipid metabolism. It has been shown that radiotherapy- and chemotherapy-induced gastrointestinal (GI) inflammation exhibits the key features of ferroptosis, including iron deposition, glutathione (GSH) depletion, glutathione peroxidase 4 (GPX4) inactivation and lipid peroxidation. In this paper, we found that ferroptosis plays an important role in radiotherapy- and chemotherapy-induced GI inflammation, and that elevating GSH levels, activating GPX4, inhibiting elevated levels of lipid peroxidation, and maintaining iron homeostasis significantly alleviated radiotherapy- and chemotherapy-induced GI inflammation. This suggests that ferroptosis may be a new target for the treatment of GI inflammation. In addition, we systematically summarize the potential mechanisms of traditional Chinese medicine (TCM) and its active ingredients in the treatment of GI inflammation, which may be effective in ameliorating radiotherapy- and chemotherapy-induced GI by acting on the key signaling pathways and mediators, such as Nrf2/HO-1, GSH/GPX4, polyunsaturated fatty acids (PUFAs), iron, and organic peroxides, which in turn inhibit the process of ferroptosis, and thereby effectively ameliorate the radiotherapy- and chemotherapy-induced GI inflammation. This finding provides a new potential approach for the treatment of such GI inflammation and demonstrates the potential value of TCM in modern medical treatment.

Saccharomyces cerevisiae, a widely utilized model organism, has seen continuous updates to its genome-scale metabolic model (GEM) to enhance the prediction performance for metabolic engineering and systems biology. This study presents an auxotrophy-based curation of the yeast GEM, enabling facile upgrades to yeast GEMs in future endeavors. We illustrated that the curation bolstered the predictive capability of the yeast GEM particularly in predicting auxotrophs without compromising accuracy in other simulations, and thus could be an effective manner for GEM refinement. Last, we leveraged the curated yeast GEM to systematically predict auxotrophs, thereby furnishing a valuable reference for the design of nutrient-dependent cell factories and synthetic yeast consortia.

Abstract Background Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. Methods Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. Results Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. Conclusion This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies.

Abstract Myasthenia gravis is an autoimmune disease characterized by pathogenic antibodies that target structures of the neuromuscular junction. However, some patients also experience autonomic dysfunction, anxiety, depression, and other neurological symptoms, suggesting the complex nature of the neurological manifestations. With the aim of explaining the symptoms related to the central nervous system, we utilized a rat model to investigate the impact of dopamine signaling in the central nervous and peripheral circulation. We adopted several screening methods, including western blot, quantitative PCR, mass spectrum technique, immunohistochemistry, immunofluorescence staining, and flow cytometry. In this study, we observed increased and activated dopamine signaling in both the central nervous system and peripheral circulation of myasthenia gravis rats. Furthermore, changes in the expression of two key molecules, Claudin5 and CD31, in endothelial cells of the blood–brain barrier were also examined in these rats. We also confirmed that dopamine incubation reduced the expression of ZO1, Claudin5, and CD31 in endothelial cells by inhibiting the Wnt/β-catenin signaling pathway. Overall, this study provides novel evidence suggesting that pathologically elevated dopamine in both the central nervous and peripheral circulation of myasthenia gravis rats impair brain–blood barrier integrity by inhibiting junction protein expression in brain microvascular endothelial cells through the Wnt/β-catenin pathway. Graphical Abstract

Abstract Enhancing the interpretability of machine learning methods for predicting material properties is a key, yet complex topic in materials science. This study proposes an interpretable convolutional neural network (CNN) to establish the relationship between the microstructural evolution and mechanical properties of non‐uniform and nonlinear multisystem dual‐phase steel materials and achieve an inverse analysis of the elastic‐plastic mechanism. This study demonstrates that the developed CNN model achieves an accuracy of 94% in predicting the stress‐strain curves of dual‐phase steel microstructures with different compositions and processes, with the mean absolute error not exceeding 50 MPa, representing merely 5.26% of the average tensile strength of dual‐phase steels in the dataset. The reverse visualization results of the CNN model indicate that, during tensile deformation, the grain boundaries maintain deformation coordination within the grains by impeding dislocation slip. This results in a significant stress concentration at the grain boundaries, with stresses at the boundaries being higher than those borne by the martensitic phase and minimal stresses in the ferrite phase. Moreover, compared with traditional crystal plasticity models, the CNN model exhibits a substantial improvement in computational efficiency. This method provides a generic plan for improving the interpretability of machine learning methods for predicting material properties and can be easily applied to other alloy systems.

This paper delves into the impact of the damage accumulation effect, which leads to the degradation of material strength and stiffness, on the seismic resistance of transmission towers. Building upon the elastic–plastic finite element theory, a mixed hardening constitutive model is derived for circular steel tubes, standard elements in transmission towers, incorporating the damage accumulation effect. A user material subroutine, UMAT, is created within the LS–DYNA framework. The program’s validity and reliability are established through axial constant–amplitude loading tests on single steel tubes. The subroutine is employed to conduct the incremental dynamic analysis (IDA) of an individual transmission tower and to contrast it with the structure utilizing the Plastic Kinematic material model, assessing the discrepancies in tower top displacements and segment damage indices (SDIs) at both macroscopic and microscopic scales. The results shows that the Plastic Kinematic model inflates the seismic performance of the transmission tower. When considering the damage accumulation effect in structural failure, the damage index of the members increases, leading to a reduction in both the structural strength and stiffness. The dynamic response in the plastic phase becomes more pronounced, and the onset of structural failure is accelerated. Consequently, structural analysis under seismic conditions should account for the damage accumulation process. Through the delineation of member and segment damage, the extent of damage to transmission tower segments can be quantitatively assessed. Subsequently, the ultimate load–bearing capacity and the most vulnerable location of the transmission tower can be ascertained. Finally, this paper provides a detailed analysis of the transmission tower collapse process under seismic action and summarizes the mechanism of collapse for the structure.

Abstract Background Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways. Methods We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed. Results We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism. Conclusion Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

Abstract Background Electrolyzed water, a green chemistry invention, is a novel disinfectant that has been widely used in the food field. Previous studies have found that slightly acidic electrolyzed water not only has a bactericidal effect but also promotes the accumulation of active substances in many types of sprouts. We developed a new type of electrolyzed water, CaCl2–HCl electrolyzed water (CHEW), which can effectively enhance the formation of glucosinolates and isothiocyanates in broccoli sprouts. The molecular mechanism by which CaCl2–HCl electrolyzed water promotes glucosinolate metabolism in broccoli sprouts during growth was investigated in this study. Results On the 4th day, the total glucosinolate content in broccoli sprouts reached 195.50 μmol/g DW under CHEW treatment. On the 8th day, compared to treatment with tap water, the contents of three isothiocyanates (sulforaphane, erucin, and 4-isothiocyanato-1-butene) in broccoli sprouts under CHEW treatment were significantly increased by 45.93, 34.04 and 185.15%, respectively. The results of calcium distribution and qRT‒PCR assessments indicated that CHEW treatment activated calcium signalling in broccoli sprouts and promoted the relative expression of glucosinolate biosynthesis-related genes through calcium sensors [calmodulin (CaM), calmodulin-like protein (CML), and calcium-dependent protein kinase (CDPK)] and the calcium-binding nuclear protein IQD1. Conclusion Our research provides new insights into the molecular mechanism of glucosinolate metabolism and a theoretical basis for cultivating high-quality broccoli sprouts. Graphical Abstract

Accurate risk prediction for myocardial infarction (MI) is crucial for preventive strategies, given its significant impact on global mortality and morbidity. Here, we propose a novel deep-learning approach to enhance the prediction of incident MI cases by incorporating metabolomics alongside clinical risk factors. We utilized data from the KORA cohort, including the baseline S4 and follow-up F4 studies, consisting of 1454 participants without prior history of MI. The dataset comprised 19 clinical variables and 363 metabolites. Due to the imbalanced nature of the dataset (78 observed MI cases and 1376 non-MI individuals), we employed a generative adversarial network (GAN) model to generate new incident cases, augmenting the dataset and improving feature representation. To predict MI, we further utilized multi-layer perceptron (MLP) models in conjunction with the synthetic minority oversampling technique (SMOTE) and edited nearest neighbor (ENN) methods to address overfitting and underfitting issues, particularly when dealing with imbalanced datasets. To enhance prediction accuracy, we propose a novel GAN for feature-enhanced (GFE) loss function. The GFE loss function resulted in an approximate 2% improvement in prediction accuracy, yielding a final accuracy of 70%. Furthermore, we evaluated the contribution of each clinical variable and metabolite to the predictive model and identified the 10 most significant variables, including glucose tolerance, sex, and physical activity. This is the first study to construct a deep-learning approach for producing 7-year MI predictions using the newly proposed loss function. Our findings demonstrate the promising potential of our technique in identifying novel biomarkers for MI prediction.

Wood defect detection is a research hotspot in the field of forestry at present. However, existing studies on wood defect detection mainly focus on detecting a single type of defect or common defects, such as knots, insect pests, and cracks, which cannot meet the processing needs of high-quality wood. Moreover, there are problems, such as low recognition rates of small-target defects and poor recognition integrity of dense defects. To address these issues, we construct a large-scale dataset containing multiple types of wood surface defects through data augmentation techniques. We also introduce the Coordinate Attention module, Transformer Encoder module, and Swin Transformer module in the YOLOv5 network structure. The backbone network CSP-Darknet53 is optimized, and BiFPN is introduced in the neck part to achieve multi-scale weighted bidirectional feature fusion. In addition, we implement three new heads: Shead, Mhead, and Lhead in the prediction part. Comparison experiments show that STC-YOLOv5 outperforms some object detection algorithms. Ablation experiments show that each module effectively improves the detection performance. Compared to YOLOv5, STC-YOLOv5 proposed in this paper improve the mAP by 3.1%. All types and scales of wood surface defects are detected better, with great potential for application in the forestry industry.

Abstract Background Long non-coding RNA (LncRNA) plays important roles in physiological and pathological processes. Identifying LncRNA–protein interactions (LPIs) is essential to understand the molecular mechanism and infer the functions of lncRNAs. With the overwhelming size of the biomedical literature, extracting LPIs directly from the biomedical literature is essential, promising and challenging. However, there is no webserver of LPIs relationship extraction from literature. Results LPInsider is developed as the first webserver for extracting LPIs from biomedical literature texts based on multiple text features (semantic word vectors, syntactic structure vectors, distance vectors, and part of speech vectors) and logistic regression. LPInsider allows researchers to extract LPIs by uploading PMID, PMCID, PMID List, or biomedical text. A manually filtered and highly reliable LPI corpus is integrated in LPInsider. The performance of LPInsider is optimal by comprehensive experiment on different combinations of different feature and machine learning models. Conclusions LPInsider is an efficient analytical tool for LPIs that helps researchers to enhance their comprehension of lncRNAs from text mining, and also saving their time. In addition, LPInsider is freely accessible from http://www.csbg-jlu.info/LPInsider/ with no login requirement. The source code and LPIs corpus can be downloaded from https://github.com/qiufengdiewu/LPInsider .

The health of the trees in the forest affects the ecological environment, so timely detection of Standing Dead Trees (SDTs) plays an important role in forest management. However, due to the large spatial scope of forests, it is difficult to find SDTs through conventional approaches such as field inventories. In recent years, the development of deep learning and Unmanned Aerial Vehicle (UAV) has provided technical support for low-cost real-time monitoring of SDTs, but the inability to fully utilize global features and the difficulty of small-scale SDTs detection have brought challenges to the detection of SDTs in visible light images. Therefore, this paper proposes a multi-scale attention mechanism detection method for identifying SDTs in UAV RGB images. This method takes Faster-RCNN as the basic framework and uses Swin-Transformer as the backbone network for feature extraction, which can effectively obtain global information. Then, features of different scales are extracted through the feature pyramid structure and feature balance enhancement module. Finally, dynamic training is used to improve the quality of the model. The experimental results show that the algorithm proposed in this paper can effectively identify the SDTs in the visible light image of the UAV with an accuracy of 95.9%. This method of SDTs identification can not only improve the efficiency of SDTs exploration, but also help relevant departments to explore other forest species in the future.

Abstract Background Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. Results We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. Conclusions This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver ( http://csbg-jlu.site/lpc/predict ) is developed to be convenient for users.

In a gravity-free or microgravity environment, liquid metals without crystalline nuclei achieve a deep undercooling state. The resulting melts exhibit unique properties, and the research of this phenomenon is critical for exploring new metastable materials. Owing to the rapid crystallization rates of deeply undercooled liquid metal droplets, as well as cost concerns, experimental systems meant for the study of liquid metal specimens usually use low-resolution, high-framerate, high-speed cameras, which result in low-resolution photographs. To facilitate subsequent studies by material scientists, it is necessary to use super-resolution techniques to increase the resolution of these photographs. However, existing super-resolution algorithms cannot quickly and accurately restore the details contained in images of deeply undercooled liquid metal specimens. To address this problem, we propose the single-core multiscale residual network (SCMSRN) algorithm for photographic images of liquid metal specimens. In this model, multiple cascaded filters are used to obtain feature information, and the multiscale features are then fused by a residual network. Compared to existing state-of-the-art artificial neural network super-resolution algorithms, such as SRCNN, VDSR and MSRN, our model was able to achieve higher PSNR and SSIM scores and reduce network size and training time.

Image super-resolution reconstruction has been widely used in remote sensing, medicine and other fields. In recent years, due to the rise of deep learning research and the successful application of convolutional neural networks in the image field, the super-resolution reconstruction technology based on deep learning has also achieved great development. However, there are still some problems that need to be solved. For example, the current mainstream image super-resolution algorithms based on single or multiple frames pursue high performance indicators such as PSNR and SSIM, while the reconstructed image is relatively smooth and lacks many high-frequency details. It is not conducive to application in a real environment. To address such problem, this paper proposes a super-resolution reconstruction model of sequential images based on Generative Adversarial Networks (GAN). The proposed approach combines the registration module to fuse adjacent frames, effectively use the detailed information in multiple consecutive frames, and enhances the spatio-temporality of low-resolution images in sequential images. While the GAN was used to improve the effect of image high-frequency texture detail reconstruction, WGAN was introduced to optimize model training. The reconstruction results not only improved the PSNR and SSIM indexes but also reconstructed more high-frequency detail textures. Finally, in order to further improve the perception effect, an additional registration loss item RLT is introduced in the GAN network perception loss. Through extensive experiments, it shows that the model proposed in this paper effectively obtains the information between the sequence images. When the PSNR and SSIM indicators are improve, it can reconstruct better high-frequency texture details than the current advanced multi-frame algorithms.

Moonlighting proteins (MPs) are a special type of protein with multiple independent functions. MPs play vital roles in cellular regulation, diseases, and biological pathways. At present, very few MPs have been discovered by biological experiments. Due to the lack of data sample, computation-based methods to identify MPs are limited. Currently, there is no de-novo prediction method for MPs. Therefore, systematic research and identification of MPs are urgently required. In this paper, we propose a multimodal deep ensemble learning architecture, named MEL-MP, which is the first de novo computation model for predicting MPs. First, we extract four sequence-based features: primary protein sequence information, evolutionary information, physical and chemical properties, and secondary protein structure information. Second, we select specific classifiers for each kind of feature. Finally, we apply the stacked ensemble to integrate the output of each classifier. Through comprehensive model selection and cross-validation experiments, it is shown that specific classifiers for specific feature types can achieve superior performance. For validating the effectiveness of the fusion-based stacked ensemble, different feature fusion strategies including direct combination and a multimodal deep auto-encoder are used for comparative purposes. MEL-MP is shown to exhibit superior prediction performance (F-score = 0.891), surpassing the existing machine learning model, MPFit (F-score = 0.784). In addition, MEL-MP is leveraged to predict the potential MPs among all human proteins. Furthermore, the distribution of predicted MPs on different chromosomes, the evolution of MPs, the association of MPs with diseases, and the functional enrichment of MPs are also explored. Finally, for maximum convenience, a user-friendly web server is available at: http://ml.csbg-jlu.site/mel-mp/.

Abstract We analyzed the scats of Amur tigers and Amur leopards, and examined their annual and seasonal food habits in Northeast China to comprehend their coexistence. Wild boar had the highest annual and seasonal consumption frequencies by the tigers, while both roe deer and sika deer were mostly preyed by the leopards annually. The three species appeared to be the key preys in terms of high proportion of consumed biomass by the two felids. Our data also revealed numerous mid-sized carnivores and small mammals included in the two felids’ food list. We used the relative abundance and biomass density estimation in prey density estimation to calculate the prey preferences of tigers and leopards, and both methods confirmed that Amur tigers strongly preferred wild boar. However, preference estimations of Amur leopards were not consistant, or even opposite to one another from the two methods. The results of the study suggested that prey preference of predators is largely determined by body size of the prey species. Variation in diet composition of the two felids suggests that resource partitioning may contribute to their coexistence.

The image-based 3D reconstruction technique has been applied in many scenarios of civil engineering, such as earthquake prevention and disaster reduction, construction monitoring, and intelligent city construction. However, the traditional technique is time-consuming, and the modeling efficiency has become a bottleneck limiting its application in emergency scenarios. In this paper, a rapid reconstruction method is proposed which combines the traditional image-based 3D reconstruction technique and an interactive graph cuts algorithm. Firstly, a sequence of images is collected around the target structure. Then, the images are preprocessed using the interactive iterative graph cuts algorithm to extract the target from each image. Finally, the resulting sequence of images is used to perform the 3D reconstruction. During the preprocessing, only a few images require manual intervention while the rest can be processed automatically. To verify the modeling accuracy of the proposed method, a column that has been destroyed is selected as a target for 3D reconstruction. The results show that compared with the traditional method, the modeling efficiency of the fast reconstruction method is doubled. In addition, the modeling accuracy is 97.65%, which is comparable to the modeling accuracy of the traditional method (97.73%); as well, by comparing the point clouds, the alignment between the two models is tremendously close, with tiny difference. The proposed rapid reconstruction method can be applied in emergency scenarios, such as rapid assessment in post-disaster situations.

Abstract The gut microbiota is characterized as a complex ecosystem that has effects on health and diseases of host with the interactions of many other factors together. Sika deer is the national level for the protection of wild animals in China. The available sequencing data of gut microbiota from feces of wild sika deer, especially for Cervus nippon hortulorum in Northeast China, are limited. Here, we characterized the gastrointestinal bacterial communities of wild (7 samples) and captive (12 samples) sika deer from feces, and compared their gut microbiota by analyzing the V3–V4 region of 16S rRNA gene using high-throughput sequencing technology on the Illumina Hiseq platform. Firmicutes (77.624%), Bacteroidetes (18.288%) and Tenericutes (1.342%) were the most predominant phyla in wild sika deer. While in captive sika deer, Firmicutes (50.710%) was the dominant phylum, followed by Bacteroidetes (31.996%) and Proteobacteria (4.806%). A total of 9 major phyla, 22 families and 30 genera among gastrointestinal bacterial communities showed significant differences between wild and captive sika deer. The specific function and mechanism of Tenericutes in wild sika deer need further study. Our results indicated that captive sika deer in farm had higher fecal bacterial diversity than the wild. Abundance and quantity of diet source for sika deer played crucial role in shaping the composition and structure of gut microbiota.

The analysis of gut microbiota using fecal samples provides a non-invasive approach to understand the complex interactions between host species and their intestinal bacterial community. However, information on gut microbiota for wild endangered carnivores is scarce. The goal of this study was to describe the gut microbiota of two leopard subspecies, the Amur leopard (Panthera pardus orientalis) and North Chinese leopard (Panthera pardus japonensis). Fecal samples from the Amur leopard (n = 8) and North Chinese leopard (n = 13) were collected in Northeast Tiger and Leopard National Park and Shanxi Tieqiaoshan Provincial Nature Reserve in China, respectively. The gut microbiota of leopards was analyzed via high-throughput sequencing of the V3–V4 region of bacterial 16S rRNA gene using the Life Ion S5™ XL platform. A total of 1,413,825 clean reads representing 4,203 operational taxonomic units (OTUs) were detected. For Amur leopard samples, Firmicutes (78.4%) was the dominant phylum, followed by Proteobacteria (9.6%) and Actinobacteria (7.6%). And for the North Chinese leopard, Firmicutes (68.6%), Actinobacteria (11.6%) and Fusobacteria (6.4%) were the most predominant phyla. Clostridiales was the most diverse bacterial order with 37.9% for Amur leopard and 45.7% for North Chinese leopard. Based on the beta-diversity analysis, no significant difference was found in the bacterial community composition between the Amur leopard and North Chinese leopard samples. The current study provides the initial data about the composition and structure of the gut microbiota for wild Amur leopards and North Chinese leopards, and has laid the foundation for further investigations of the health, dietary preferences and physiological regulation of leopards.

In engineering construction, the accurate estimation of the investment cost can provide a reasonable basis for the formulation and decision-making of the construction scheme. In this paper, the wind-storage combined operation power station is taken as the research object, the investment cost estimation model is established, and the combined operation mode is analysed to obtain the annual power generation. Finally, according to the investment cost and annual power generation, the cost per unit of electricity model is established. In the past, wind farm and pumped storage power station are usually studied as two independent individuals, but in practical application, there are power conversion, regulation and control operations between them, which are interrelated and interactive. This paper regards the two as a whole and analyses their annual power generation under different cooperative operation modes. The research results of this paper can provide a theoretical basis for the preliminary cost estimation of engineering construction, so as to better determine the construction scheme of power plant.