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Establishing a scientifically effective systemic financial risk early warning model is of great significance for prudently mitigating systemic financial risks and enhancing the efficiency of financial supervision. Based on the measurement of systemic financial risk and the network sentiment index of 47 financial institutions, this study adopted the "decomposition–reconstruction–integration" approach, utilizing techniques such as extreme-point symmetric empirical mode decomposition (ESMD), empirical mode decomposition (EMD), variational mode decomposition (VMD), hierarchical clustering, fast independent component analysis (FastICA), attention mechanism, bidirectional long short-term memory neural network (BiLSTM), support vector regression (SVR), and their combination, to construct a systemic financial risk prediction model. The empirical results demonstrate that decomposing and reconstructing relevant indicators before predicting systemic financial risks can enhance prediction accuracy. Among the proposed models, the ESMD-HFastICA-BiLSTM-Attention model exhibits superior performance in systemic financial risk early warning. [ABSTRACT FROM AUTHOR]

Dioscoreae hypoglaucae Rhizoma (DH) and Dioscoreae spongiosae Rhizoma (DS) are two similar Chinese herbal medicines derived from the Dioscorea family. DH and DS have been used as medicines in China and other Asian countries for a long time, but study on their phytochemicals and bioactive composition is limited. This present study aimed to compare the chemical compositions of DH and DS, and explore the anti-xanthine oxidase components based on chemometric analysis and spectrum–effect relationship. Firstly, an HPLC method was used to establish the chemical fingerprints of DH and DS samples, and nine common peaks were selected. Then, hierarchical clustering analysis, principal component analysis and orthogonal partial least squares discriminant analysis were employed to compare and discriminate DH and DS samples based on the fingerprints data, and four steroidal saponins compounds (protodioscin, protogracillin, dioscin, gracillin) could be chemical markers responsible for the differences between DH and DS. Meanwhile, the anti-xanthine oxidase activities of these two herbal medicines were evaluated by xanthine oxidase inhibitory assay in vitro. Pearson correlation analysis and partial least squares regression analysis were subsequently used to investigate the spectrum–effect relationship between chemical fingerprints and xanthine oxidase inhibitory activities. The results showed that four steroidal saponins, including protodioscin, protogracillin, methyl protodioscin and pseudoprogracillin could be potential anti-xanthine oxidase compounds in DH and DS. Furthermore, the xanthine oxidase inhibitory activities of the four selected inhibitors were validated by anti-xanthine oxidase inhibitory assessment and molecular docking experiments. The present work provided evidence for understanding of the chemical differences and the discovery of the anti-xanthine oxidase constituent of DH and DS, which could be useful for quality evaluation and bioactive components screening of these two herbal medicines. [ABSTRACT FROM AUTHOR]

As air pollution becomes more and more serious, PM2.5 is the primary pollutant, inevitably attracts wide public attention. Therefore, a novel PM2.5 concentration forecasting method based on linear fuzzy information granule_dynamic time warping_hierarchical clustering algorithm (LFIG_DTW_HC algorithm) and generalized additive model is proposed in this paper. First, take 30 provincial capitals in China for example, the cities are divided into seven regions by LFIG_DTW_HC algorithm, and descriptive statistics of PM2.5 concentration in each region are carried out. Secondly, it is found that the influencing factors of PM2.5 concentration are different in different regions. The input variables of the PM2.5 concentration forecasting model in each region are determined by combining the variable correlation with the generalized additive model, and the main influencing factors of PM2.5 concentration in each region are analyzed. Finally, the empirical analysis is conducted based on the input variables selected above, the generalized additive model is established to forecast PM2.5 concentration in each region, the comparison of the evaluation indexes of the training set and the test set proves that the novel PM2.5 concentration forecasting method achieves better prediction effect. Then, the generalized additive model is established by selecting cities from each region, and compared with the auto-regressive integrated moving average (ARIMA) model. The results show that the novel PM2.5 concentration forecasting method can achieve better prediction effect on the premise of ensuring high accuracy. [ABSTRACT FROM AUTHOR]

Based on authorized patents of China's artificial intelligence industry from 2013 to 2022, this paper constructs an Industry–University–Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm's innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers. [ABSTRACT FROM AUTHOR]

The phenomenon of polarized development among regional cities has sparked extensive contemplation and indicated a need for research on multi-source regional networks. However, such research faces two obstacles: the absence of quantitative measurement of differences in network structures and the lack of a thorough examination of the degree of city clustering and the dynamics of community composition in hierarchical networks. Thus, we identified 16 cities in the Chengdu–Chongqing Economic Circle (CCEC) as the spatial units to examine the spatial network structures of population, resources, and transportation and the integrated spatial network structure. Using social network analysis, this paper describes the structural characteristics of the three networks (population, resource, and transportation), followed by an analysis of their collective and hierarchical network clustering characteristics, and explores the driving mechanisms and factors that make up each network model. Our results show the following: (1) All three networks exhibit an "east dense, west sparse" characteristic, but there are differences in the layouts of the core cities in terms of the three networks. (2) The clustering characteristics of the hierarchical networks are more pronounced than those of the overall network. The results of the analysis combined with the network formation mechanisms can help effectively plan the future coordinated development of the CCEC. [ABSTRACT FROM AUTHOR]

A high-performance liquid chromatograph with diode array detector was established for the simultaneous determination of five phenylethanoid glycosides in Syringa pubescens Turcz. The optimal chromatographic conditions were achieved on a Zorbax C18 column using gradient elution with 0.5% aqueous acetic acid and acetonitrile as the mobile phase at the flow rate of 1.0 mL/min. The detection wavelength was developed as follows: 0–10 min, 276 nm; 10–45 min, 332 nm. The validation of the method including linearity, precision, stability, accuracy, repeatability and recovery was tested. The chemometric analysis including hierarchical cluster analysis and principal component analysis was employed to investigate the similarity and difference of samples from different geographical origin. The results revealed that S. pubescens samples were divided into four clusters based on the phenylethanoid glycosides contents. Antioxidant activity of extract was measured using three different methods including α,α-diphenyl-β-picrylhydrazyl and 2,2-azino-bis(3-ethylbenzthiazoline-6-sulphonic acid) radical scavenging assays, and ferric reducing antioxidant power assay. Furthermore, different phenylethanoid glycosides exhibited different contribution to antioxidant capacities. This study provides a foundation for the quality evaluation and offers scientific data for the utilization of S. pubescens resources. [ABSTRACT FROM AUTHOR]

A recent surge of patent applications among public hospitals in China has aroused significant research interest. A country's healthcare innovation capacity can be measured by its number of patents. This paper explores the link between the number of patents and ten independent variables. Multicollinearity was carefully detected and removed by using the variable selection method and LASSO regression, respectively. The Poisson model and the negative binomial model were proposed to analyze the patent data. Three goodness of fit tests, the Pearson test, the deviance test, and the DHARMa non-parametric dispersion test, were conducted to investigate if the model has a good fit. After discovering four clusters by conducting agglomerative hierarchical clustering, these two models were replaced by the negative binomial mixed model. The likelihood ratio test was used to determine which model is more appropriate and the results reveal that the negative binomial mixed model outperforms both the Poisson model and the negative binomial model. Three variables, number of health technicians per 10,000 people, financial expenditure on science and technology as well as number of patent applications per 10,000 health personnel, have a significantly positive relationship with the number of patents in Chinese tertiary public hospitals. [ABSTRACT FROM AUTHOR]

Areca (Areca catechu L.) is one of the most important cash crops in China and is considered the fourth most widely used addictive substance. In addition, areca is widely used in traditional and herbal medicines. The major characteristics of the fruit are affected by its genetic background and growth environment. The growing environment in different regions will impact the quality of agricultural products and the processing quality. The quality of areca is not only the basis of its commercialization development and processing quality, but also is an important basis for the scientific planting of areca. Therefore, determining the quality of areca will provide evidence for scientific planting and more optimal applications. We evaluated the quality of areca by comparing the differences in physicochemical characteristics using principal component analysis (PCA) and hierarchical cluster analysis. A total of 165 arecas, in the same growth period, were collected from 11 main producing regions in Hainan Province. Our results illustrate that the physicochemical characteristics of areca in different regions were significantly different. The PCA was conducted using 10 quality indexes, and three principal components were extracted to reflect 80% of the original variables. The first principal component mainly reflected the fruit shape quality, the second principal component mainly reflected the hardness quality, and the third principal component mainly reflected the functional component quality. The relationship between each producing region and the principal component could be obtained intuitively from the principal component score plots. The arecas in Wanning and Wenchang were larger and their cellulose content was greater than in other areas, indicating that they were more suitable for processing. In contrast, the arecas in Baoting, Wuzhishan, Danzhou, Tunchang, and Dongfang had a greater arecoline content than the other areas, making them more suitable for use as medicinal materials. Hierarchical cluster analysis classified the 11 producing regions into five categories based on the measured parameters, which was consistent with the results of the PCA score plots. These results could provide information to improve the use of areca in China. [ABSTRACT FROM AUTHOR]

The Dongying Depression is an important petrolifeous province, with diverse source rocks and complex petroleum distribution patterns. A total of 32 crude oils were analyzed by the gas chromatographyemass spectrometry and isotopic compositions to better understanding the petroleum systems in the study area. Three oil types were classified by hierarchical cluster analyses. Type I and II oils have closely correlation with the discovered source rocks, which have been confirmed to be mainly derived from the lower third and upper forth member of the Eocene Shahejie Formation source rocks (Es3L and Es4U), respectively. Obviously, type III oils contain abundant gammacerane, tricyclic terpanes and C29 steranes and have lower values of δ13C than type I and II oils, indicating a completely different source rock and biological origins. Until recently, type III oils fail to match any of the discovered source rock, which contains main contribution of aquatic organism or/and bacteria inputs. In addition, the spacial distribution of these three oil types were discussed. Type I oils mainly distributed in the Es3 and Es4 reservoirs that closed to the generative kitchens. Type II oils occurred in the Es4 reservoirs in the sourthern slope of the depression, which probably caused by lateral migration along the horizontal fractures and sandstone layers within the Es4 interval. Differently, type III oils in the sourthern slope of the depression were mainly discovered in the Eocene Kongdian or Ordocician reservoirs, which suggests great exploration potential of deep underlying strata. [ABSTRACT FROM AUTHOR]

BACKGROUND: Chrysanthemum is one of the most important and popular ornamentals over the world. Chrysanthemum drink is a type of traditional healthy drink like Chinese tea. Owing to the differences in the chemical compositions, different chrysanthemum varieties have different medicinal effects on human health. Thus, the identification of different chrysanthemum varieties is very important and necessary. This study aims to distinguish seven chrysanthemum varieties that are widely used in China. First, total lipids were obtained from chrysanthemums. After that, lipid profiles were characterized using ultra‐high‐performance liquid chromatography hyphenated with a Q Exactive™ high resolution–accurate‐mass mass spectrometer. RESULTS: A total of 163 lipid molecular species from 17 types of lipid classes in seven varieties of chrysanthemums were determined. Principal component analysis indicated that three lipid molecules, lysophosphatidylethanolamine(18:2) (LPE(18:2)), LPE(16:0), and phosphatidic acid(18:2/18:3) (variable importance in projection >3, P < 0.001), can be used as potential biomarkers to distinguish seven chrysanthemum varieties. Hierarchical cluster analysis showed that the lipid molecular profiles of 'Gongju' were most similar to 'Jinzijianju', followed by 'Huaibaiju', 'Boju', 'Hangbaiju', 'Chuju', and 'Fubaiju'. CONCLUSION: This comprehensive analysis provided a new method to identify chrysanthemum varieties through the perspective of lipidomics combined with chemometrics. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Cropland productivity can be greatly improved to ensure national food security in China, particularly under the quantitative constraints of farmland resources. Among them, land consolidation can be an important way for the high cropland productivity in recent years. But, the spatial differentiation patterns of land consolidation can pose a great impact on cropland productivity in large-scale regions. Taking the Yangtze River Economic Zone as an example, this study aims to determine the impact of land consolidation on cropland productivity change using MODIS-EVI remote sensing data. The S-G filtering and threshold were used to extract the cropland productivity (i.e., single-crops productivity, the first and second season productivity of double crops) in the study area from 2001-2017. Then, 80 920 projects of land consolidation were collected (including land development, consolidation, and reclamation projects) with the location and attribute information using the national rural land consolidation monitoring and supervision system. The contribution rate of land consolidation was further analyzed for the cropland productivity in counties using a geographically weighted regression model, together with the spatial differentiation of the impact degree. Finally, the impact of land consolidation was spatially partitioned on the cropland productivity using the hierarchical clustering model. The optimization strategies of land consolidation were also proposed for each zone, according to the cropland utilization characteristics. The results showed that: 1) There was a significant spatial divergence trend of cropland productivity. Specifically, the productivity of single-crops and double-crops in the first and second seasons presented a decelerating increase, an accelerating increase, and an increase followed by a decrease, respectively, with the largest increase in the productivity in the upper reaches of the Yangtze River. There was a significant decrease in the productivity of the single-crops and the second season productivity of double-crops in the lower reaches; 2) The average contribution of land development, allocation, and reclamation to the change of single-crops productivity were 7.38%, 4.44%, and 2.80%, respectively; the average contribution to the change of first season productivity of double-crops were 6.21%, 5.04%, and 3.34%, respectively; and the average contribution to the change of second season productivity of double-crops were 7.10%, 7.15%, and 3.69%, respectively. 3) The Yangtze River economic belt was divided into the consolidation areas of the southwest mountain, the Sichuan basin, the southern hilly, and the lower Yangtze River plain, according to the cropland productivity and the impact mechanism of land consolidation. A better optimization was also provided for the land consolidation of each region. In addition, some findings were also needed to further explain: firstly, the land allocation was the least effective to promote cropland productivity improvement among all types of projects. The reason behind this issue can be expected to continue. Another phenomenon is that the second season productivity of double-crops was generally negatively correlated with the land consolidation indicators, has the implementation of land consolidation accelerated the phenomenon of “double to single”? What is the mechanism behind this phenomenon? Finally, it is very necessary to improve the efficiency of the causal inference using a geographically weighted regression model. [ABSTRACT FROM AUTHOR]

Grifola frondosa polysaccharides (GFPs) from different regions in China were characterized and compared using HPSEC-MALLS-RID and saccharide mapping based on HPAEC-PAD analysis for achieving and improving its quality control. The results showed that HPSEC chromatograms and molecular weight distributions of GFPs were similar. The average contents of each polysaccharide fraction (Peaks 1, 2, and 3) showed that Peak 3 was the main component and much higher than the other two polysaccharide fractions, which also contained protein. The result of saccharide mapping showed that α-1,4-glycosidic, β-1,4-glycosidic and few β-1,3-glycosidic linkages were existed in GFPs. The similarity result showed that HPAEC-PAD fingerprints of the oligosaccharide fragments after hydrolysis by endoglycosidase were certainly different, especially α-amylase with a mean similar index of only 0.781 ± 0.207. The result of hierarchical cluster analysis (HCA) showed that different batches of GFPs from China can be divided into different clusters. Furthermore, immune-enhancing activity based on RAW 264.7 cells showed significant differences among different GFPs. Based on grey relational analysis (GRA), the fractions of Peak 3 were regarded as the major contributors to its immuno-enhancing activity in GFPs. Overall, the implications from these results were found to be stable, comprehensive, and valid for improving the quality control of GFPs. [ABSTRACT FROM AUTHOR]

China is ranked as the number one maritime connected country in the world. This study attempts to analyse the characteristics of its 155 maritime trading partners. Five components of maritime connectivity have been used for the analysis. A hierarchical clustering technique is applied to group the trading partners into distinct meaningful clusters. As China's main maritime competitors within the East Asian region, the analysis is also applied to Singapore and Hong Kong. The clustering technique produces three distinct clusters for China and Singapore and two for Hong Kong. The results reveal the importance of direct maritime connectivity, with China having the highest number of trading partners with zero transhipment (including its top 15 trading partners), compared to Singapore and Hong Kong. The analysis is further extended by applying ordered logistic regression. The results show that the logistic performance index (LPI) and exports/imports have a positive and significant influence on clusters. Conclusions are drawn as to the potential for implementing strategies and policy interventions on the basis of the emergent clusters, particularly the need for Hong Kong to improve all five components of maritime connectivity in order to compete with China and Singapore. [ABSTRACT FROM AUTHOR]

Thirty oil samples collected from the eastern Fukang Sag were analyzed geochemically for their biomarkers and carbon isotopic compositions. The chemometric methods of principal component analysis and hierarchical cluster analysis, employed to thirteen parameters indicating source and depositional environment, classified the oil samples into three genetically distinct oil families: Family A oils were mainly derived from lower aquatic organisms deposited in a weakly reducing condition of fresh–brackish water, Family B oils came from a source containing predominantly terrigenous higher-plant organic matter laid down in an oxidizing environment of fresh water, and Family C oils received sources from both terrigenous and marine organic matter deposited in a weakly oxidizing to oxidizing environment of brackish water. Indirect oil–source correlations implied that Family A oils were probably derived from Permian source rocks, Family B oils originated mainly from Jurassic source rocks, and Family C oils had a mixed source of Carboniferous and Permian. Biomarker maturity parameters revealed that all three families of oils were in the mature stage. However, Family A oils were relatively less mature than Family B and Family C oils. [ABSTRACT FROM AUTHOR]

Purpose: The association of patent foreman ovale (PFO) and cryptogenic stroke has been studied for years. Although device closure overall decreases the risk for recurrent stroke, treatment effects varied across different studies. In this study, we aimed to detect sub-clusters in post-closure PFO patients and identify potential predictors for adverse outcomes.Methods: We analyzed patients with embolic stroke of undetermined sources and PFO from 7 centers in China. Machine learning and Cox regression analysis were used.Results: Using unsupervised hierarchical clustering on principal components, two main clusters were identified and a total of 196 patients were included. The average age was 42.7 (12.37) years and 64.80% (127/196) were female. During a median follow-up of 739 days, 12 (6.9%) adverse events happened, including 6 (3.45%) recurrent stroke, 5 (2.87%) transient ischemic attack (TIA) and one death (0.6%). Compared to cluster 1 (n = 77, 39.20%), patients in cluster 2 (n = 119, 60.71%) were more likely to be male, had higher systolic and diastolic blood pressure, higher body mass index, lower high-density lipoprotein cholesterol and increased proportion of presence of atrial septal aneurysm. Using random forest survival (RFS) analysis, eight top ranking features were selected and used for prediction model construction. As a result, the RFS model outperformed the traditional Cox regression model (C-index: 0.87 vs. 0.54).Conclusions: There were 2 main clusters in post-closure PFO patients. Traditional cardiovascular profiles remain top ranking predictors for future recurrence of stroke or TIA. However, whether maximizing the management of these factors would provide extra benefits warrants further investigations. [ABSTRACT FROM AUTHOR]

Objectives: Provincial comprehensive atlas is a complex knowledge system. It uses thematic maps to comprehensively display the resource endowment and social development level of a province. We reconstruct the large, complex and unstructured contents of the atlas to establish the atlas content tupu, explore the basic content organization rules of provincial comprehensive atlas in China. Methods: First, we construct the vocabulary vectors of thematic subjects presented in atlas, calculate their semantic similarities, and extract the standard expressions of subject words. Second, following the compile order of “map group, map sheet, and cartographic index”, the semantic hierarchical clusters of subject words are figured out, and the content map of atlas are constructed in a tree-wised graph. Finally, the frequent subgraphs of content tupu among provincial atlases are examined to form an atlas fingerprint to identify the common features of subject expressions in the comprehensive atlas of China’s provinces. Results: It is shown that the thematic contents of provincial comprehensive atlas are organized hierarchically, and the fingerprint tupu can be used as a framework to guide the thematic selection and content organization during the comprehensive atlas compilation. With the benefit of content tupu and fingerprint tupu, it can be further revealed that the provincial comprehensive atlas in China has obvious clustering characteristics and diversity characteristics in content expression. Conclusions: The research results provide a basis for the design of provincial comprehensive atlas. In the future, we will explore the content tupus of different types of atlas to enrich the theory of atlas design and compilation. [ABSTRACT FROM AUTHOR]

The Chang 7 member of the Upper Triassic Yanchang Formation is an important source rock in the Ordos Basin, NW China. Previous studies of the unit have in general focused on the relatively deep‐water (∼25 m) anoxic, OM‐rich lacustrine mudstones which form the main Mesozoic source rock at fields in the south of the basin. However, this paper presents an integrated geochemical investigation of the relatively shallow‐water Chang 7 facies in a study area around Jiyuan field in the western part of the basin in order to evaluate its source rock potential. The results show that the Chang 7 source rock in the study area has a high content of Type II OM, and is interpreted to have been deposited in a suboxic‐anoxic lacustrine setting with a mixed input of aquatic and terrigenous organic matter. The results of 1D modelling of a well in the study area showed that the Chang 7 member entered the oil generation window from the Middle Jurassic. Oil‐oil correlations based on hierarchical cluster analysis and correspondence analysis showed that crude oils generated by deep‐water Chang 7 source rocks from wells in the south of the basin can be distinguished from Soxhlet‐extracted petroleum from reservoir sandstones in the study area. The compositional differences are inferred to be due to variations in source rock facies compositions. An oil – source rock correlation study showed that the shallower‐water Chang 7 source rock in the western part of the basin generated the hydrocarbons in core extracts of reservoir sandstones from wells in this area. The shallow‐water Chang 7 facies may therefore constitute an effective oil‐prone source rock in the western Ordos Basin. [ABSTRACT FROM AUTHOR]

Background: Because of the certain overlapping of the genus Bupleurum, the identification features are few and the delimitation among species is difficult, Bupleurum taxonomy is under frequent revisions. In this research, hierarchical cluster analysis based on morphology characters is done to establish a dendrogram and illustrate the taxonomic relationship among Bupleurum species (Apiaceae) from China. Materials and Methods: The fruit morphology of 17 Bupleurum taxa from China was investigated with a scanning electron microscope (SEM) and optical microscopy (OM). The hierarchical cluster analysis based on 41 morphological character states belonging to 9 characters was done to establish a dendrogram and illustrate the taxonomic relationship among the studied species of Bupleurum (Apiaceae) from China. Results: Based on fruit morphological characteristics, it is possible to separate the Bupleurum species into the following four groups. The research indicates that the classification of Bupleurum in China is closely related to geographical distribution, which is divided into Southwestern and Northeastern Bupleurum. Conclusion: The fruit morphological characteristics are variable enough to distinguish species between Bupleurum, such as fruit length, width, shape, colour, the curvature of the outer periclinal and so on in this article. Morphological characters could be probably the taxonomical standard of intraspecific and interspecific delamination, which is significant for understanding the classification of Bupleurum. [ABSTRACT FROM AUTHOR]

To improve the accuracy of remote sensing inversion modeling of soil organic matter (SOM) at the field scale, this study selected a 41.3 ha (hm2) field in the black soil region of northeastern China as the study area. The spatial differences of soil physical and chemical properties, reflection spectra and topographic factors, as well as the relationship between spectral index, topographic factor and SOM were analyzed. Multiscale segmentation and hierarchical clustering were also conducted based on measured SOM samples, bare-soil Sentinel-2A images and 4-m-resolution digital elevation model (DEM) data. Then, the stepwise regression SOM prediction models were constructed for the entire study area and its subregions. The results led to the following conclusions: (1) SOM presents significant spatial heterogeneity in the study area; (2) the SOM prediction accuracy of single model for the entire study area was low (R2 = 0.16, RMSE.cal = 1.61, and RMSE.val = 1.45); and (3) entire study area can be divided into four subregions, i.e., the "sedimentary area," "deposition-buffer area," "erosion-buffer area" and "erosion area," the SOM prediction model was established for each subregion according to its SOM content and spectral as well as topographic characteristics, and SOM prediction accuracy for the entire study area clearly improved (R2 = 0.58, RMSE.cal = 1.17, and RMSE.val = 1.30). In this study, prediction models employing the subregion method were established and represent a new approach for SOM inversion research in the future. The results can be used as a reference for the study of SOM inversions of farmland blocks in the black soil region of northeastern China and provide theoretical and technical support for soil carbon pool estimations and precision fertilization. [ABSTRACT FROM AUTHOR]

The rapid development of the economy promotes the increasing of interactions between cities and forms complex networks. Many scholars have explored the structural characteristics of urban spatial interaction networks in China and have conducted spatio-temporal analyzes. However, scholars have mainly focused on the perspective of static networks and have not understood the dynamic spatial interaction patterns of Chinese cities. Therefore, this paper proposes a research framework to explore the urban dynamic spatial interaction patterns. Firstly, we establish a dynamic urban spatial interaction network according to monthly migration data. Then, the dynamic community detection algorithm, combined with the Louvain and Jaccard matching method, is used to obtain urban communities and their dynamic events. We construct event vectors for each urban community and use hierarchical clustering to cluster event vectors to obtain different types of spatial interaction patterns. Finally, we divide the urban dynamic interaction into three urban spatial interaction modes: fixed spatial interaction pattern, long-term spatial interaction pattern, and short-term spatial interaction pattern. According to the results, we find that the cities in well-developed areas (eastern China) and under-developed areas (northwestern China) mostly show fixed spatial interaction patterns and long-term spatial interaction patterns, while the cities in moderately developed areas (central and western China) often show short-term spatial interaction patterns. The research results and conclusions of this paper reveal the inter-monthly urban spatial interaction patterns in China, provide theoretical support for the policy making and development planning of urban agglomeration construction, and contribute to the coordinated development of national and regional cities. [ABSTRACT FROM AUTHOR]

The genomic breed composition (GBC) reflects the genetic relationship between individual animal and ancestor breeds in composite or hybrid breeds. Also, it can estimate the genomic contribution of each breed (ancestor) to the genome of each individual animal. Using genomic SNP information to estimate Ningxiang pig GBC is of great significance. First of all, GBC was widely used in cattle and had significant effects, but there is almost no using experience in Chinese endemic pig breeds. Importantly, High‐density SNPs are expensive but can be economized by deploying a relatively small number of highly informative SNP scattered evenly across the genome. Moreover, the impact of low‐density SNPs selection strategy on estimating the GBC of individual animals has not been fully explained. Using SNP data from different databases and organizations, we established reference (N = 2015) and verification (N = 302) data sets. Twelve successively smaller SNP panels (500, 1K, 5K, 10K) were built from those SNP in the reference data by three selection methods (uniform, maximized the Euclidean distance (MED) and random distribution method). For each panel, the GBC of Ningxiang pigs in the reference dataset was estimated. Then combining Shannon entropy and the GBC results, the optimal panel (the 10K SNP panel constructed by MED method) was picked out to estimate the GBC of verification Ningxiang pig, which detected that 230 individuals were purebred Ningxiang pigs and the remaining 72 impure individuals contained 6.44% blood related with Rongchang pigs and 4.09% with Bamaxiang pigs in the verification Ningxiang population. Finally, the genetic structure analysis of verification population was performed combining with the results of GBC, multi‐dimensional scaling (MDS) analysis and hierarchical cluster analysis. These results showed: (a) GBC could accurately identify purebred Ningxiang pigs and, scientifically, calculate the genomic contribution of each breed of each hybrid animal. (b) GBC could carry out population genetic structure and understand the genetic background of Ningxiang pigs. Such findings highlight a variety of opportunities to better protect and identify other endangered local breeds in China facing the same situation as Ningxiang pig and provide more accurate, economical and efficient new technical support in GBC estimation breeding work. [ABSTRACT FROM AUTHOR]

Temperature regionalization is important for guiding the development of agriculture and forestry in a region. However, few existing studies are able to objectively regionalize temperature in terms of both the temperature change and temperature difference. In both cases, we use the ERA5 2 m temperature reanalysis dataset to construct the temperature correlation network by traversing the winter anomalies with higher amplitudes for all possible pairs of grid points. On the basis of the temperature correlation network, the first and second‐grade regionalization is performed using the spin glass algorithm and agglomerative hierarchical clustering, respectively. Our results derived from the network topology analysis indicate that there are a few teleconnections in the network and that the nodes are mainly connected to neighbouring nodes. Some nodes in Tibet, Qinghai and western Sichuan Province have independent temperature change, and some nodes in Guizhou and Jiangxi Provinces tend to connect more nodes with an average distance of >500 km. The results of temperature regionalization show that the boundaries of the first‐grade regionalization are consistent with the relief and the distribution of major mountains in China, and the spatial distribution coincides with the four core regions with the highest convergence coefficient of temperature change (TCC) of nodes and 10 regions with relatively high TCC. The second‐grade regionalization is mainly located in the different temperature zones. In contrast to previous works on temperature regionalization, this study provides a new integration framework that further considers both the convergence in the temperature change and the closeness of the temperature between regions and finally objectively regionalizes temperature in China. [ABSTRACT FROM AUTHOR]

Xinyang Maojian (XYMJ) green tea is one of the top ten teas in China, and the consumers prefer spring tea due to its umami taste and pleasurable aroma. However, the knowledge about temporal variation of the volatile compounds in XYMJ green teas harvested during different seasons is very limited. In the present work, the main non-volatile compounds that endowed the taste and volatile compounds responsible for the aroma in XYMJ green teas harvested during the spring and autumn seasons were determined. The average contents of free amino acids (FAA) were significantly higher and gradually declined in the spring teas, whereas the caffeine was significantly lower and gradually increased in the spring teas. A total of 39 volatile compounds of six chemical classes were detected in XYMJ green teas, and they displayed various change trends during the spring and autumn seasons, among which 15 volatile compounds were identified as the key odorants based on odor activity value (OAV). The highest OAV of 2195.05 was calculated for the violet-like smelling trans-β-ionone followed by decanal, nonanal, dimethyl sulfide, linalool, geraniol and naphthalene. The OAVs of geraniol, (Z)-3-hexenyl hexanoate, heptanal, benzaldehyde and hexanal in XYMJ spring teas were higher than XYMJ autumn teas. The hierarchical clustering analysis indicated that XYMJ green teas were divided into three clusters and the quality of XYMJ green teas changed greatly within spring season. Harvest season is a crucial factor affecting the flavor quality of XYMJ green teas. [ABSTRACT FROM AUTHOR]

With evidence-based measures, COVID-19 can be effectively controlled by advanced data analysis and prediction. However, while valuable insights are available, there is a shortage of robust and rigorous research on what factors shape COVID-19 transmissions at the city cluster level. Therefore, to bridge the research gap, we adopted a data-driven hierarchical modeling approach to identify the most influential factors in shaping COVID-19 transmissions across different Chinese cities and clusters. The data used in this study are from Chinese officials, and hierarchical modeling conclusions drawn from the analysis are systematic, multifaceted, and comprehensive. To further improve research rigor, the study utilizes SPSS, Python and RStudio to conduct multiple linear regression and polynomial best subset regression (PBSR) analysis for the hierarchical modeling. The regression model utilizes the magnitude of various relative factors in nine Chinese city clusters, including 45 cities at a different level of clusters, to examine these aspects from the city cluster scale, exploring the correlation between various factors of the cities. These initial 12 factors are comprised of 'Urban population ratio', 'Retail sales of consumer goods', 'Number of tourists', 'Tourism Income', 'Ratio of the elderly population (> 60 year old) in this city', 'population density', 'Mobility scale (move in/inbound) during the spring festival', 'Ratio of Population and Health facilities', 'Jobless rate (%)', 'The straight-line distance from original epicenter Wuhan to this city', 'urban per capita GDP', and 'the prevalence of the COVID-19'. The study's results provide rigorously-tested and evidence-based insights on most instrumental factors that shape COVID-19 transmissions across cities and regions in China. Overall, the study findings found that per capita GDP and population mobility rates were the most affected factors in the prevalence of COVID-19 in a city, which could inform health experts and government officials to design and develop evidence-based and effective public health policies that could curb the spread of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

• Nonparametric kernel density estimator was used to calculate SGI. • Significant spatial variation in groundwater drought was observed across the region. • Groundwater drought is weakly connected to meteorological and agricultural droughts. • Agricultural irrigation and groundwater extraction influence groundwater drought. Groundwater drought is a significant type of hydrological drought and manifests as abnormal groundwater shortages as a result of prolonged drought propagation. This type of drought typically exhibits a longer duration than meteorological and agricultural droughts and can be severe in arid and semiarid regions. Human activities like pumping can further complicate the detection and characterization of groundwater drought. This study used long-term groundwater level monitoring data from 1991 to 2018 for the Yinchuan region to calculate a standardized groundwater level index and applied hierarchical clustering and the run theory to assess groundwater drought. Additionally, the relationship between groundwater and meteorological/agricultural droughts were investigated, and the factors driving groundwater drought were analyzed. Based on the results, regional groundwater droughts are becoming increasingly severe. In this study, there are regional differences in the trends of the standardized groundwater level index, which could be divided into three clusters (C1, C2, and C3). Wells in C2 are located near the Yellow River and show a shorter groundwater drought duration (mean value: 3.16 months), which means that these wells can recover from drought more quickly than those in the other clusters. Meteorological and agricultural droughts show a weak correlation with groundwater drought (with average cross-correlation coefficients of 0.03 and 0.25, respectively), and human activities most likely were the causes of regional groundwater droughts. Thus, a comprehensive understanding of regional groundwater droughts should consider human activities, in addition to the geographical location and natural environments. This research establishes a scientific foundation for managing regional groundwater resources and offers novel insights for future groundwater drought studies in the context of human activities. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multi-scale pore structure and shale components at medium-to-low maturity studied. • A complementary set of experimental tools, including SANS, utilized for a holistic view. • Statistical approaches of HCA and PCA utilized to interpret experimental data. • Three groups of shales classified with various pore structure and component contribution. An important issue concerning the sustainable production of shale oil is the characterization of pore structure at various scales, but there is an insufficient understanding of the coupled relationship between pore structure and shale components. Integrated analyses using field emission-scanning electron microscopy, mercury intrusion porosimetry, nitrogen physisorption, and small angle neutron scattering were conducted on nine shale samples from the Upper Triassic Chang 7 Member in Ordos Basin to investigate the relationship between multi-scale pore structure and shale minerals and organic matter (OM). The results show that the clays minerals (mainly mixed illite–smectite) are usually combined with OM to form complex OM-hosted pores, and inorganic pores consist of both inter- and intra-particle pores (mostly related to clay platelet clusters). Three groups of shales classified by hierarchical cluster analyses (HCA) vary widely in pore structure, with the highest pore volume and largest pore diameter observed in a decreasing order of Group 1, Group 2, and Group 3. Principal component analysis (PCA) shows that brittle minerals (mainly quartz) have preserved interP pores that contribute to the development of high pore volumes and large pore-throat diameters, and ductile clays minerals and OM tend to fill pores and reduce pore diameter and volume. The complex nature of shale porosity is influenced by multiple factors, and hence accurate and comprehensive characterization requires the use of diverse methods including both experimental and theoretical analyses. An integrated approach to elucidating the factors controlling pore structure in medium-to-low maturity shale will help with efficient shale oil production. [ABSTRACT FROM AUTHOR]

The popularity of electric vehicles (EVs) brings environmental benefits, but their hard-to-estimate stochastic charging behaviors places additional diversity on grid load management. This paper proposes a procedure to identify typical charging load profiles (CLPs) via large scale charging session data from charging stations (CSs). The daily CLPs are computed from charging sessions, and a comprehensive similarity metric based on the weighting of Euclidean and Pearson correlation coefficient is proposed to achieve better clustering. The clustering is performed using the Clustering LARge Applications (CLARA) algorithm to accommodate large sample scenarios. Subsequently, hierarchical clustering of CSs is performed based on possible CLPs，and their CLPs are estimated by Monte Carlo simulation. The performance of the proposed method is tested and evaluated with over 340,000 charging sessions from 109 CSs in Wuhan at central China, and seasonal differences in CLPs are explored. The results show that the method of mining typical CLPs from charging sessions is effective, 17 typical CLPs are identified in different seasons, which provide effective information on the fluctuation and magnitude of daily power demand, the charging power demand also shows significant seasonal differences, and good accuracy is achieved by dividing the CSs into different groups for load estimation. • A method for identifying typical charging load profiles from charging session data is proposed. • 17 typical charging load profiles for charging stations in Wuhan are reported. • Monte Carlo simulation combined with typical charging load profiles enables good estimation of charging loads. • Seasonal differences in charging loads are compared. [ABSTRACT FROM AUTHOR]

• Development of a novel registration method combining ULS and TLS LiDAR data. • Hierarchical clustering of trunk point clouds generates accurate tree maps. • Validated in 40 sample plots in subtropical forests. • Achieved registration accuracy is better compared to existing methods. • Significantly reduced registration time to approximately 50 s per 30 × 20 m plot. Unmanned aerial vehicle laser scanning (ULS) and terrestrial laser scanning (TLS) provide complementary, non-destructive approaches to acquire three-dimensional forest structure information. Registration of their point clouds enables the reconstruction of complete vertical structure of forests. Current registration methods are primarily designed to register different TLS scans and thus are not applicable to ULS-TLS registration directly. In this study, the proposed method first generated multi-layer tree maps from ULS and TLS data using hierarchical clustering, then extracted Fast Point Feature Histograms (FPFH) features for each cluster point based on spatial relationships in the tree maps. After that, a point-to-point matching strategy was used to obtain the transformation matrix of each layer between ULS and TLS trunk point clouds and the best matrix from all layers was finally selected for fine registration. The algorithm was validated in 40 sample plots in Guangxi province of China. Our findings indicated that both high-density ULS and TLS data generate accurate tree maps compared to manually counted tree number, with a Concordance Correlation Coefficient (CCC) of 0.961 and 0.973, respectively. The proposed method performed well in registration accuracy and time efficiency, and achieved a higher matching score (0.945 > 0.928) and lower RMSE (0.144 < 0.151) than manual registration. The average registration time per sample plot of 600 m2 was 48.9 s, with 19.4 s dedicated to coarse registration. This research highlights the potential of clustering-based registration methods for effectively aligning ULS-TLS point cloud data in forests, laying the foundation for further technological advancements in forest vertical structure reconstruction. [ABSTRACT FROM AUTHOR]

Sea buckthorn (Hippophaë rhamnoides L.) is well-known for its high content of flavonol glycosides (FGs), yet accurately determining the quantity of these nutrients present in the berries remains a challenge due to the significant variability of these nutrients and lack of reference standards. In this study, flavonol glycosides in sea buckthorn berries were identified and quantified using ultra-performance liquid chromatography Q-exactive high resolution mass spectrometry (UPLC-QE HRMS). The results showed that the total content of flavonol glycosides ranged from 39.5 to 240.2 mg/100 g fresh berries. The wild berries from Sichuan had remarkably high contents and unique profiles of flavonol glycosides. The main flavonol glycosides in all of the samples were isorhamnetin glycosides and quercetin glycosides, the predominant flavonol glycosides were different between different samples. The sPLS-DA statistical analysis indicated that cultivated and wild sea buckthorn can be distinguished based on their distinct composition and content of flavonol glycosides. The HCA clustered 16 high expression levels of FGs, which were revealed in a wild sample obtained from Sichuan. • High resolution and high sensitivity analysis was carried out on UPLC-QE HRMS. • 33 flavonol glycosides identified and quantified by UPLC-QE HRMS in sea buckthorn. • Differences of the flavonol glycosides between cultivated and wild samples were analyzed. • sPLS-DA analysis revealed clear distinction between cultivated and wild sea buckthorn. • High expression flavonol glycosides identified by hierarchical cluster analysis. [ABSTRACT FROM AUTHOR]

Chlorinated paraffins (CPs) can accumulate in sediment and pose risks to ecological systems and human health. The Haihe River Basin is one of the seven main river basins in China and is mainly in the Beijing–Tianjin–Hebei region, which is densely populated and very urbanized. There is therefore a high probability of CP pollution in the Haihe River Basin. However, CP pollution and the environmental risks posed by CPs in the Haihe River are not well understood. In this study, the concentrations of short-chain CPs (SCCPs) and medium-chain CPs (MCCPs) in sediment from six rivers in the Haihe River Basin system were determined using two-dimensional gas chromatography electron-capture negative ionization mass spectrometry. The total SCCP and MCCP concentrations in the sediment samples ranged from 131.83 to 1767.71 and from 89.72 to 1442.82 ng/g dry weight, respectively. The total organic carbon content did not significantly correlate with the CP concentrations. The dominant SCCP congener groups were C 10 Cl 6–7 and the dominant MCCP congener groups were C 14 Cl 7–8. Significant relationships (R = 0.700, p < 0.05) were found between the SCCP and MCCP concentrations, indicating that SCCPs and MCCPs may have similar sources. Hierarchical cluster analysis and principal component analysis indicated that sediment in the study area was contaminated with CPs through the use of the CP-42 and CP-52 commercial products in industrial processes and human activities. The ecological risks posed by CPs were assessed and SCCPs were found to pose high risks in the Yongding New River but moderate risks in the other rivers. MCCPs were found to pose minimal risks to the aquatic environment at most of the sampling points. [Display omitted] • CPs in the sediment in the Haihe River Basin were investigated. • The dominant SCCP and MCCP congener groups were C 10 Cl 6–7 and C 14 Cl 7–8 , respectively. • SCCPs and MCCPs possibly derived from the production and use of CP-42 and CP-52. • SCCPs were high ecological risks in the Yongding New River. [ABSTRACT FROM AUTHOR]

This article examines multimodal frame construction and presents comparative research regarding Germany's mediated national image. We conducted a quantitative content analysis of 2455 news posts (NUSA = 1722, NChina = 733) published by leading newspapers on Twitter (USA) and Weibo (China) between 2007 and 2019. The results reveal that news texts attribute varying degrees of salience to the dimensions of national image. Chinese news texts portray Germany more positively among most indicators compared to US news texts. News visuals assume more complementary or dramatizing functions in the US context and more elaborated or illustrative roles in the Chinese context. A hierarchical cluster analysis shows how Germany's national image is constructed through textual contents, visual denotations, and text-image relations in multimodal news. German cultural attractions, football excellence, social diversity, and economic performance are commonly visible across both nations, whereas political debate, crisis, and international controversy are largely absent from Chinese news frames. Contexts shape multimodal frames, where the uniformity of coverage is the result of globalized news production and shared journalistic standards such as (visual) news values relating to eliteness, impact, and consonance. National differences are interpreted by contextual factors of political homophily, journalism culture, and platform affordance. [ABSTRACT FROM AUTHOR]

Based on the introduction of complex social network theory and methods, this study reveals enterprises' patent layouts to help Chinese enterprises distinguish the core competitors. This study uses the social network analysis method and the system cluster analysis method to systematically analyze the macro technology development trend and competitors' micro technology innovation points. Based on the structure of enterprise resource investment, a two-dimensional matrix with the keyword "similarity-technological competition intensity" was constructed to accurately identify BYD's core technological competitors. In China, BYD company should regard GX company and ND company as core technology competitors, pay attention to the technology development direction and patent layout of YW company and HD company, and pay attention to the patent dynamics of GLM company and YQ company in specific technological innovation points. [ABSTRACT FROM AUTHOR]

Fructus Gardeniae, known as Zhi-zi in China, has been used as Chinese herbal medicine and functional health food for thousands of years. Fructus Gardeniae Grandiflorae, named as Shui-zhizi, is a counterfeit herb of Fructus Gardeniae. In order to discriminate these two varieties, based on ultra-high-performance liquid chromatography, an analysis method of fingerprints of Fructus Gardeniae and Fructus Gardeniae Grandiflorae was established. With hierarchical clustering analysis and principal component analysis, they were separated into two groups. Analyzed with partial least squares discriminant analysis, there were differences in chemical compositions between Fructus Gardeniae and Fructus Gardeniae Grandiflorae. Six compounds, crocin I, genipin-1- β - D -gentiobioside and four other unknown compositions were identified as differential marker compositions between them. Furthermore, seven active substances in them were determined simultaneously. Thus, an integral method of ultra-high-performance liquid chromatography fingerprint combined with chemometrics analysis and quantitative assessment was established. It could be utilized in characterization, quality evaluation of Fructus Gardeniae and could be applied for discriminating Fructus Gardeniae from Fructus Gardeniae Grandiflorae. [ABSTRACT FROM AUTHOR]

The identification of the oil-source correlation plays a significant role in petroleum exploration and development. In this study, we identify the oil-source correlation by a hierarchical cluster analysis method combined with traditional methods. The results shed light on the oil-source correlation in Minfeng area and revealed the oil migration and accumulation process. The crude oil in different structural belts and different horizons has different geochemical characteristics. According to the four types of crude oil and their planner distribution, it was considered that the crude oil mainly migrates along with favorable sand bodies and unconformity surfaces in the lateral direction and then charged and accumulated in the glutenite of Sha3 and Sha4 members since the area from sag to Yan Jia Oil and the gas field was lacking of oil source faults. Further analysis shows that the traps of fault blocks in Yong'anzhen are formed in the same phase, while the crude oil generated in the early stage is charged and accumulated in the fault block of the near source. Along with increasing of the buried depth of source rocks, the overlying source rocks gradually entered into the hydrocarbon generation phase, when crude oil started to charge in the fault blocks farther away. [ABSTRACT FROM AUTHOR]

The aim of this study is to design a novel machine learning model named Agglomerative Hierarchical Clustering algorithm based on Overlapped Interval Divergence distance measure (AHC-OLID), for modelling and assessing landslide susceptibility. The AHC-OLID algorithm is proposed to combat the limitations of many clustering algorithms in modelling and assessing landslide susceptibility, including: pre-defining the number of clusters; sensitivity to the clusters properties and noisy data; as well as difficulty in processing rainfall data. The proposed algorithm addresses these issues by integrating the traditional Agglomerative Hierarchical Clustering (AHC) and Overlapped Interval Divergence distance measure (OLID) methods into landslide susceptibility assessment to enhance its performance. It considers the clusters sizes as well the distances between them and tends to avoid taking into consideration small clusters which are very far from other clusters in the dataset. It is also insensitive to the variation in sizes, variances and shapes of the clusters, which makes the algorithm more advantageous. Besides, the AHC-OLID algorithm makes use of the OLID distance function to process the rainfall data, which takes two factors into consideration: distance between their centers as well as relative size of their overlapped area. Applying this new approach in Baota District, China, produced significant improvement in assessment of landslide susceptibility than previous models. Moreover, the Landslide susceptibility map constructed based on AHC-OLID algorithm can be a useful tool for landslide controlling strategies for proper land use and planning. [ABSTRACT FROM AUTHOR]

Green tea is one of the main teas in China, which is unfermented and retains more natural substances of fresh tea leaves. This is the preliminary study of application of 'fingerprints' based on differences in component composition of green tea. Five green teas from different areas in eastern China are analyzed, which are processed by microwave-assisted solvent (ethanol) extraction method to obtain tea polyphenols, flavonoids, polysaccharides, pigments (thearubigins, theaflavins, theabrownins). The results show that the component composition of five green teas are varied from each other; based on these contents varieties, we have constructed a 'fingerprint' and applied linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA) to assist in the identification of these five green teas. This method does not require large, expensive instruments (such as high performance liquid chromatograph, gas chromatograph, mass spectrometer, etc.), and is easy to use, which provides a new avenue for the identification of tea. [ABSTRACT FROM AUTHOR]

The identification of the oil-source correlation plays a significant role in petroleum exploration and development. In this study, we identify the oil-source correlation by a hierarchical cluster analysis method combined with traditional methods. The results shed light on the oil-source correlation in Minfeng area and revealed the oil migration and accumulation process. The crude oil in different structural belts and different horizons has different geochemical characteristics. According to the four types of crude oil and their planner distribution, it was considered that the crude oil mainly migrates along with favorable sand bodies and unconformity surfaces in the lateral direction and then charged and accumulated in the glutenite of Sha3 and Sha4 members since the area from sag to Yan Jia Oil and the gas field was lacking of oil source faults. Further analysis shows that the traps of fault blocks in Yong'anzhen are formed in the same phase, while the crude oil generated in the early stage is charged and accumulated in the fault block of the near source. Along with increasing of the buried depth of source rocks, the overlying source rocks gradually entered into the hydrocarbon generation phase, when crude oil started to charge in the fault blocks farther away. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the demographics, clinical aspects, and major organ involvement of patients with late-onset Behçet's syndrome (BS) in a tertiary center in China. Methods: We conducted a cross-sectional study of consecutive BS patients in Huadong Hospital of Fudan University from September 2012 to January 2020. We compared clinical variables between patients with disease onset before and after 40 years of age. The relative risks (RRs) of clinical variables were calculated between the two age groups. Moreover, a hierarchical cluster analysis was conducted according to 29 variables to determine homogeneous subgroups in patients with late-onset BS. Results: We enrolled 152 late-onset BS patients, with a median age at onset of 47 years (interquartile range, IQR: 43–52 years). There is a higher prevalence of intestinal ulcers in late-onset BS than in early-onset BS (RR 1.47), but a lower prevalence of ocular involvements (RR 0.54) and folliculitis (RR 0.46). Female sex was associated with genital ulcers, erythema nodosum, and arthritis. Four clusters (C1–C4) were formed. C1 (n = 71), the largest cluster, was defined as the mucocutaneous group, C2 (n = 20) as the arthritis group, C3 (n = 39) as the gastrointestinal group, in which all patients presented with intestinal lesions, and five cases with esophageal ulcers. In C4 (n = 22), showing a mixture of uveitis and vascular lesions, 15 patients presented with uveitis and 8 had vascular lesions, and 1 case had central nervous system lesions. Conclusion: Four phenotype clusters were identified. Patients with skin lesions comprised the largest cluster, while gastrointestinal, panuveitis, and cardiovascular clusters are the most commonly involved organs in late-onset BS patients. Key Points • Our analysis demonstrated the phenotype discrepancy between early and late onset groups. • Four phenotype clusters were identified, with gastrointestinal, panuveitis and cardiovascular clusters representing commonly involved organs. [ABSTRACT FROM AUTHOR]

Objectives: The aim of the study is to identify clusters of lymphocyte subsets within treatment-naive systemic lupus erythematosus (SLE) patients and evaluate the potential association of these clusters with disease activities. Methods: We conducted a cross-sectional study of consecutive 143 treatment-naive SLE patients in the Affiliated Hospital of Nantong University, China. We used hierarchical cluster analysis to classify individuals into clusters based on circulating lymphocyte subset proportions (CD3+CD4+T cell, CD3+CD8+T cell, CD19+B cell, and CD3-CD16 + CD56 NK cell) via R software. Demographic variables, clinical manifestations, laboratory variables, and disease activities were compared among clusters. Results: The SLE patients (median age 35 (26–48) years, 90.9% female) were divided into four clusters. The clustering features were as follows: cluster 1 (B high), cluster 2 (CD4 high), cluster 3 (CD8 high), and cluster 4 (NK high). SLE patients in cluster 1 showed the highest incidence of arthritis (70.6%, 34.2%, 48.3%, and 42.9% in clusters 1, 2, 3, and 4, respectively; P = 0.046), and patients in cluster 3 and cluster 4 showed significantly a higher incidence of nephritis (35.3%, 25.0%, 48.3%, and 61.9% in in clusters 1, 2, 3, and 4, respectively; P = 0.008). Patients in cluster 2 suffered from lower SLE Disease Activity Index (SLEDAI) score (12.1 ± 5.0, 10.3 ± 5.6, 12.2 ± 4.6, and 14.4 ± 7.3 in clusters 1, 2, 3, and 4, respectively; P = 0.046). Regression analysis indicated that, compared with patients in cluster 2, patients in cluster 1 exhibited higher rate of arthritis (OR 4.53, 95% CI 1.38–14.86, P = 0.013), while patients in cluster 3 (OR 2.85, 95%CI 1.15–7.08, P = 0.024) and cluster 4 (OR 4.93, 95%CI 1.76–13.85, P = 0.002) exhibited higher rate of nephritis. Conclusion: This study supports the existence of lymphocyte subset clusters with different clinical features in treatment-naive SLE patients, which could help to differentiate between various subsets of SLE. Key Points • Lymphocyte subsets may occur in a pattern of cluster in treatment-naive SLE patients. [ABSTRACT FROM AUTHOR]

Recently, the water quality of the second longest River in China (the Yellow river) is deteriorating severely. To investigate the heavy metals contaminated degrees, potential risks and pollution sources of heavy metals in the river, we measured the concentrations of 8 heavy metals (Fe, Mn, Cu, Ni, Zn, Cr, Pb and Cd) from 122 surface sediment samples collected along the upstream Yellow River. According to the results of enrichment factor (EF) analysis and the geoaccumulation index (Igeo) evaluation, we found Cd accumulated significantly for moderately contaminated level and the other heavy metals existed at uncontaminated levels. We conducted risk assessment by using calculation of the sediment quality guidelines (TEC-PEC SQGs) and potential ecological risk index (RI), and the results implied that the concentrations of Ni, Cr and Cd had potential harm for aquatic organisms and the upstream Yellow River had integral no toxic. Moreover, the results of multivariate analysis (principle component analysis, hierarchical cluster analysis and correlation analysis) indicated that Fe, Mn, Cu, Zn, Cr and Cd in the river primarily originated from natural sources, while Ni and Pb mainly derived from different human activities. In addition, partial Cu and Cd come from anthropogenic sources of Ni discharge. [ABSTRACT FROM AUTHOR]

Objectives: The visualization of cyberspace information is of great significance to reveal the rules and promote the cognition of cyberspace. Visualizing the nodes and their topological relationships in cyber- space without preprocessing often result in a large number of point coincidences and line crossings. Al- though the visualization result can be optimized by the node layout algorithms, the divided edge bundling technologies, the backbone extraction technologies and the methods of multi -scale representation of net- work routing, the spatial characteristic information of these nodes as cyberspace point cluster features can- not be maintained on a micro level of network structure. Therefore, by analyzing and quantifying various types of characteristic information of point cluster features in cyberspace from four aspects of statistical in-formation, metric information, topology information and thematic information, two generalization methods are proposed. Methods: (1) An aggregation method based on hierarchical clustering. First, organize the point cluster features into a hierarchical tree of which every final node community is in an appropriate and well-balanced size through multi-layer K-means clustering according to their topology information. Then ag-gregate these features by their community.(2)A selection method based on node importance measuring. After measuring the comprehensive importance of the each feature from the aspects of maintaining the structural stability and its service coverage area, select the more important ones and delete the relatively unimportant considering the whole network topology.Results: A series of generalization experiments at different spatial scales were performed on the point cluster features incyberspace of an area in a central part of China through these two methods of aggregation and selection. The result shows that these methods can realize the auto-matic generalization of the point cluster features in cyberspace at different spatial scales while maintaining the spatial characteristics of these features.Conclusions: The purpose is to provide an effective data processing method of quantitatively expressing the characteristics of cyberspace and accelerating the process of optimizing visualization results. [ABSTRACT FROM AUTHOR]

Nowadays, urban multimodal big data are freely available to the public due to the growing number of cities, which plays a critical role in many fields such as transportation, education, medical treatment, and land resource management. The successful completion of poverty-relief work can greatly improve the quality of people's life and ensure the sustainable development of the society. Poverty is a severe challenge for human society. It is of great significance to apply machine learning to mine different categories of poverty-stricken households and further provide decision support for poverty alleviation. Traditional poverty alleviation methods need to consume a lot of manpower, material resources, and financial resources. Based on the density-based spatial clustering of applications with noise (DBSCAN), this paper designs the hierarchical DBSCAN clustering algorithm to identify and analyze the categories of poverty-stricken households in China. First, the proposed method adjusts the neighborhood radius dynamically for dividing the data space into several initial clusters with different densities. Then, neighbor clusters are identified by the border and inner distances constantly and aggregated recursively to form new clusters. Based on the idea of division and aggregation, the proposed method can recognize clusters of different forms and deal with noises effectively in the data space with imbalanced density distribution. The experiments indicate that the method has the ideal performance of clustering, which identifies the commonness and difference in characteristics of poverty-stricken households reasonably. In terms of the specific indicator "Accuracy," the accuracy increases by 2.3% compared with other methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel PCEC concept evaluates provincial carbon emission characteristics in China. • PCEC addresses multiple climate governance issues for China's dual carbon goals. • PCEC undergoes a thorough evaluation via a multi-dimensional causal decomposition model. • PCEC indicators enable tailored emission reduction policies for provincial clusters. • PCEC indicators potentially applicable to regions or countries beyond China. This study defines Provincial Carbon Emission Characteristics (PCEC) as the socioeconomic conditions influencing provincial carbon emissions, the potential for carbon neutrality, and carbon management strategies. Given China's commitment to peak carbon emissions and to achieve carbon neutrality, the development of a robust and versatile PCEC evaluation system is imperative. However, previous studies that rely on single-objective evaluation models are limited. To address this gap, we propose a multi-objective PCEC evaluation system using a novel three-layer approach. This approach enhances the scientific rigor, comprehensiveness, and comparability of PCEC evaluation indicators (PCEC-EIs) by segregating the extraction process into three layers: (1) a factor layer, which identifies critical socioeconomic conditions; (2) a quantitative layer, which selects optimal quantitative indicators for each identified condition; and (3) a standardized layer, which standardizes these indicators, thereby improving their comparability. By applying hierarchical cluster analysis, our study classified 30 Chinese provinces into 7 distinct clusters, each with unique carbon emission characteristics. Further investigation of these clusters across various climate governance issues revealed the versatility of our multi-objective PCEC evaluation system. Our system supports the identification of provinces qualified to reach the emission peak earlier than 2030, differentiation of carbon intensity reduction efforts during the carbon peaking stage, and exploration of various emission reduction pathways to achieve carbon neutrality. Hence, our findings can guide the creation of bespoke emission-reduction strategies for each province; thus, aiding China's national ambition to achieve peak carbon emissions and carbon neutrality. Importantly, our three-layered approach has broader implications beyond China, potentially assisting other regions or countries in grappling with similar climate-mitigation challenges. [ABSTRACT FROM AUTHOR]

Green tea is one of the main teas in China, which is unfermented and retains more natural substances of fresh tea leaves. This is the preliminary study of application of 'fingerprints' based on differences in component composition of green tea. Five green teas from different areas in eastern China are analyzed, which are processed by microwave-assisted solvent (ethanol) extraction method to obtain tea polyphenols, flavonoids, polysaccharides, pigments (thearubigins, theaflavins, theabrownins). The results show that the component composition of five green teas are varied from each other; based on these contents varieties, we have constructed a 'fingerprint' and applied linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA) to assist in the identification of these five green teas. This method does not require large, expensive instruments (such as high performance liquid chromatograph, gas chromatograph, mass spectrometer, etc.), and is easy to use, which provides a new avenue for the identification of tea. [ABSTRACT FROM AUTHOR]

Swamp eel or rice field eel (Monopterus albus) is one of the most economically valuable freshwater fishes in China. Based on the body colour patterns, Chinese rice field eels are commonly classified into three strains, including deep‐yellow strain (DY), pale‐yellow strain (PY) and steel‐grey strain (SG). Reports have shown that DY strain is superior to the other two strains at growth rate and fecundity. In the present study, RNA‐Seq technology was employed to profile liver transcriptomes of these three strains, and differentially expressed genes (DEGs) were identified. The hierarchical clustering analysis showed that expression patterns of liver transcripts among the three strains were different. A total of 3,296 DEGs, 3,043 DEGs and 143 DEGs were identified, respectively, from DY versus SG, DY versus PY and PY versus SG. The three comparison groups shared three DEGs, of which one DEG (batf) played a vital role in immune system. Functional enrichment analyses of DEGs in DY versus PY and DY versus SG revealed that significantly enriched Gene Ontology terms and KEGG pathways were mainly involved in innate immune responses and lipid metabolism. Additionally, most of DEGs related to immunity were mainly up‐regulated in DY compared to PY and SG. The above results indicated that the DY strain might have strong adaptability and resistance to disease, which might support their superiority for better growth and reproduction. [ABSTRACT FROM AUTHOR]

Until now, there has been little research on the intestinal microbial community of horseshoe crabs. To fill this gap, we investigated the microbiome composition of the Chinese horseshoe crab, Tachypleus tridentatus, and the mangrove horseshoe crab, Carcinoscorpius rotundicauda. We sequenced the 16S rRNA gene of intestinal bacterial species and compared the microbial community structure and diversity. Next, we show that the total effective bacterial sequence was 36,865 reads, and the average annotated operational taxonomic unit (OTU) number was 240. Through hierarchical clustering analysis and principal coordinate analysis samples from two horseshoe crab species, we found that the intestinal flora of the same horseshoe crab species was relatively concentrated, while the microbiome of a different horseshoe crab species were significantly separated. Cluster analysis showed that two samples, one from Chinese horseshoe crabs and one from mangrove horseshoe crabs, had similar microbial community structure, while other samples were relatively discrete. The gut microbiota of the mangrove horseshoe crab were dominated by the phyla Tenericutes (42.71%), Firmicutes (24.27%), and Proteobacteria (20.39%), while the top three phyla in the Chinese horseshoe crab intestinal tract were Tenericutes (57.19%), Proteobacteria (22.14%), and Bacteroidetes (7.38%). To intuitively understand the similarity and overlap of the OTU composition of each group, we performed Venn diagram analysis. The two species shared 284 OTUs, accounting for 81.8% of the total. This indicates that although there is high similarity between mangrove and Chinese horseshoe crab in gastrointestinal microbial community structure, there are also some differences, which deserve further discussion. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity has become a prominent problem worldwide; however, few population-based studies have been conducted among older Chinese with multimorbidity. This study aimed to examine the prevalence of multimorbidity and explore its common patterns among a nationally representative sample of older Chinese.Methods: This study used data from the China Health and Retirement Longitudinal Study and included 19,841 participants aged at least 50 years. The prevalence of individual chronic diseases and multimorbidity during 2011-2015 were evaluated among the entire cohort and according to residential regions and gender. The relationships between participants' demographic characteristics and multimorbidity were examined using logistic regression model. Patterns of multimorbidity were explored using hierarchical cluster analysis and association rule mining.Results: Multimorbidity occurred in 42.4% of the participants. The prevalence of multimorbidity was higher among women (odds ratio [OR] = 1.31, 95% confidence interval [CI]: 1.13-1.51) and urban residents (OR = 1.14, 95% CI: 1.02-1.27) than their respective counterparts after accounting for potential confounders of age, education, smoking, and alcohol consumption. Hierarchical cluster analysis revealed four common multimorbidity patterns: the vascular-metabolic cluster, the stomach-arthritis cluster, the cognitive-emotional cluster, and the hepatorenal cluster. Regional differences were found in the distributions of stroke and memory-related disease. Most combinations of conditions and urban-rural difference in multimorbidity patterns from hierarchical cluster analysis were also observed in association rule mining.Conclusion: The prevalence and patterns of multimorbidity vary by gender and residential regions among older Chinese. Women and urban residents are more vulnerable to multimorbidity. Future studies are needed to understand the mechanisms underlying the identified multimorbidity patterns and their policy and interventional implications. [ABSTRACT FROM AUTHOR]

The ultrasonic extraction (UE) technology, possessed the advantages of effective, energy‐saving, and environmental‐friendly, was applied to extract the anthocyanin from Lycium ruthenicum (LR). The extraction parameters of UE were optimized by response surface methodology (RSM) with Box–Behnken design (BBD). Anthocyanin composition in LR fruits grown in China was systematically identified and quantified by HPLC‐ESI‐MS. The result showed that PRG was the major anthocyanin, and delphinidin, petunidin, and malvidin were the major anthocyanidins in LR fruits. There was the same anthocyanin composition of LR and great variation in anthocyanins content of LR from different areas in China. However, there was no significant difference between wild and cultivated LR in the same region. A clear separation of LR according to geographical origins was revealed by hierarchical cluster analysis (HCA) and principal component analysis (PCA), and the discrimination model for the anthocyanin concentrations were developed using these two analysis methods. Furthermore, on‐line HPLC‐DPPH assay and scavenging activity of three kinds of radicals (DPPH·, ·OH, and O2-·) in vitro were well applied to evaluate the antioxidant activity of the LR anthocyanin extract (LRAE). And its results indicated the LRAE could be a credible antioxidant agent for applications in cosmetics, food, and medicine. [ABSTRACT FROM AUTHOR]

Modern mechanical equipment is very important to agricultural production. The introduction of modern machinery and equipment into the production process of traditional Chinese medicine is an effective way to solve the contradiction between the expansion of production scale of traditional Chinese medicine industry and the reduction of labor force. The mechanization development of traditional Chinese medicinal materials production is in the initial stage. The main goal at this stage is to realize the "from scratch" of the relevant technical equipments. However, due to the varieties of traditional Chinese medicine, complex production process, regional differences and other factors, it is difficult to judge the demand for mechanized production of traditional Chinese medicine and lack of methods. How to evaluate and analyze the the differences in the demand for mechanized development is of great significance to the research of technical equipment, the implementation of supply side reform and the promotion of the production and development of traditional Chinese medicine. According to the characteristics of traditional Chinese medicinal materials production, combined with the analysis of degree of mechanized operation, status of technology and equipment and levels of user demand, this paper constructs the evaluation index system for the development of technical and equipment of traditional Chinese medicinal materials production. The degree of mechanized operation indicates the ratio of mechanized operation area to the total planting area. The status of technology and equipment is alculated by weighting the five indexes of advance ment, economy, applicability, reliability and security. The levels of user demand is divided according to the proportion of users with demand, users with demand but no machinery available and the proportion of users with demand in different production process. Then an evaluation model combined with entropy weight method and hierarchical cluster analysis method is formed, and after classifying by the hierarchical cluster analysis method, the model is expressed functionally based on the Fisher discriminant method. In empirical research, the demonstration base of national comprehensive experimental station of traditional Chinese medicinal materials is selected as the research object, eight production processes of sowing, transplanting, fertilization, intertillage, plant protection, harvesting, cleaning and drying are selected for evaluation Through the investigation in 23 provinces of China, the production processes of traditional Chinese medicinal materials production are categorized into three types. The harvest, intertillage, and transplanting belonged to the type A, which have the highest demand level. The seeding is classified as type B, and the drying, cleaning, plants protection and fertilization are type C. The phenomenons of “no machinery available” and “unsuitable to use” are obvious for type A, and there is an urgent demand for the development of technology and equipment, the evaluation results are consistent with the investigation results. According to the further calculation results based on discriminant Function I, the central values of type A, type B and type C are -2.868, 1.904 and 1.675 respectively, which shows that the evaluation model of development demands classification can be accurately expressed by quantitative mathematical functions. The machinery of the type A production processes represented by harvest should be developed in priority in traditional Chinese medicinal materials production. The research and development of special machinery for the production of traditional Chinese medicinal materials should take the road of combining independent innovation research with the introduction of other main grain production machinery. The model proposed in this paper reflects the reasons and details at the same time of forming classification, which can provide reference for the development demand evaluation of other crops or regional agricultural machinery technology and equipment with backward mechanization development. [ABSTRACT FROM AUTHOR]

Purpose: Distress screening using measures of patient-reported outcomes (PRO) has been introduced in China and is increasingly recognized as contributing to whole-patient care. We carried out a multi-centered cross-sectional survey of Chinese cancer inpatients to explore the symptom burden, symptom clusters, and risk factors of distress.Method: Patients were recruited from five hospitals in four provinces. The Distress Assessment and Response Tool (DART) was used as the screening tool. Demographic and medical information was collected. Descriptive analysis, the chi-square test, logistics regression analysis, and hierarchical clustering analysis were used.Results: Totally 1045 valid questionnaires were collected (83.6% validity ratio). Low well-being (39.4%), lack of appetite (35.4%), tiredness (32.9%), pain (21.1%), and anxiety (19.8%) were the top five symptoms. Patients in Ci County had a heavier symptom burden than patients at other sites. Depression, anxiety, nausea, drowsiness, and pain were considered pain-illness symptoms; lack of appetite, low well-being, tiredness, and shortness of breath were considered fatigue-illness symptoms. Social difficulty was a risk factor for all symptoms. A high proportion of suicide ideation (38.8%) and suicide intention (10.5%) was identified among patients with potential depression.Conclusion: The high symptom burden of Chinese cancer inpatients indicates the necessity of distress screening; well-designed screening programs such as the multidimensional DART and its acceptability in China should be further explored. Social difficulty has a universal impact on patients' well-being, and psychosocial care should be integrated into holistic symptoms management. [ABSTRACT FROM AUTHOR]