Multidimensional Analysis Research Articles

Phytoplankton Diversity, Spatial Patterns, and Photosynthetic Characteristics Under Environmental Gradients and Anthropogenic Influence in the Pearl River Estuary

The Pearl River Estuary (PRE) is one of the world’s most urbanized subtropical coastal systems. It presents a typical environmental gradient suitable for studying estuarine phytoplankton communities’ dynamics and photosynthetic physiology. In September 2018, the maximum photochemical quantum yield (Fv/Fm) of phytoplankton in different salinity habitats of PRE (oceanic, estuarine, and freshwater zones) was studied, revealing a complex correlation with the environment. Fv/Fm of phytoplankton ranged from 0.16 to 0.45, with taxa in the upper Lingdingyang found to be more stressed. Community composition and structure were analyzed using 18S rRNA, accompanied by a pigment analysis utilized as a supplementary method. Nonmetric multidimensional scaling analysis indicated differences in the phytoplankton spatial distribution along the estuarine gradients. Specificity-occupancy plots identified different specialist taxa for each salinity habitat. Dinophyta and Haptophyta were the predominant taxa in oceanic areas, while Chlorophyta and Cryptophyta dominated freshwater. Bacillariophyta prevailed across all salinity gradients. Canonical correlation analysis and Mantel tests revealed that temperature, salinity, and elevated nutrient levels (i.e., NO3−-N, PO43−-P, and SiO32−-Si) associated with anthropogenic activities significantly influenced the heterogeneity of community structure. The spatial distribution of phytoplankton, along with in situ photosynthetic characteristics, serves as a foundational basis to access estuarine primary productivity, as well as community function and ecosystem health.

Myocardial injury and clinical outcome in octogenarians after non–ST-elevation myocardial infarction

IntroductionThe aim of this study was to analyze age-associated myocardial injury and clinical outcome after non-ST-elevation myocardial infarction (NSTEMI).MethodsThis prospective, multicenter study consists of 440 patients with NSTEMI enrolled at 7 centers. All patients were treated with primary percutaneous coronary intervention and underwent cardiac magnetic resonance (CMR) imaging 1–10 days after study inclusion. CMR parameters of myocardial injury and clinical outcome were evaluated by creating 2 subgroups: <80 years vs. ≥80 years. The clinical endpoint was the 1-year incidence of major adverse cardiac events (MACE) consisting of death, re-infarction and new congestive heart failure.ResultsElderly patients ≥80 years accounted for 13.9% of the study population and showed a divergent cardiovascular risk profile compared to the subgroup of patients <80 years. CMR imaging did not reveal significant differences regarding infarct size, microvascular obstruction, left ventricular ejection fraction or multidimensional strain analysis between the study groups. At 1-year follow-up, MACE rate was significantly increased in patients ≥80 years compared to patients aged <80 years (19.7% vs. 9.6%; p = 0.019). In a multiple stepwise logistic regression model, the number of diseased vessels, aldosterone antagonist use and left ventricular global longitudinal strain were identified as independent predictors for MACE in all patients, while there was no independent predictive value of age regarding 1-year clinical outcome.ConclusionThis prospective, multicenter analysis shows that structural and functional myocardial damage is similar in younger and older patients with NSTEMI. Furthermore, in this heterogeneous but also clinically representative cohort with reduced sample size, age was not independently associated with 1-year clinical outcome, despite an increased event rate in patients ≥80 years.

Corpus-based Multi-dimensional Analysis of the Register Features of Financial News

Nowadays, people rely heavily on financial news media to get financial updates. But as is well known, for non-native speakers of English, in order to fully understand such news, they also need to learn some register features of such financial news besides adequate English language knowledge. The purpose of this study is to explore and analyze the linguistic features of English financial news, and to help readers effectively understand financial news. Specifically, this study employes multi-feature/multi-dimensional analysis (henceforth MF/MD) method to analyze the register characteristics of English financial news based on the theory of register. Through analyzing the self-compiled corpus of financial news, it is found that that the financial news has four major register features: interactivity, persuasion, information and professionalism. These findings will shed lights on the understanding of financial news as well as the learning and teaching of financial/business English.

Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials

Machine learning interatomic potential (MLIP) has been widely adopted for atomistic simulations. While errors and discrepancies for MLIPs have been reported, a comprehensive examination of the MLIPs’ performance over a broad spectrum of material properties has been lacking. This study introduces an analysis process comprising model sampling, benchmarking, error evaluations, and multi-dimensional statistical analyses on an ensemble of MLIPs for prediction errors over a diverse range of properties. By carrying out this analysis on 2300 MLIP models based on six different MLIP types, several properties that pose challenges for the MLIPs to achieve small errors are identified. The Pareto front analyses on two or more properties reveal the trade-offs in different properties of MLIPs, underscoring the difficulties of achieving low errors for a large number of properties simultaneously. Furthermore, we propose correlation graph analyses to characterize the error performances of MLIPs and to select the representative properties for predicting other property errors. This analysis process on a large dataset of MLIP models sheds light on the underlying complexities of MLIP performance, offering crucial guidance for the future development of MLIPs with improved predictive accuracy across an array of material properties.

Service Oikos as a complex self-perpetuating system: a bibliometric study of service ecosystems

PurposeThis study aims to address the following research questions: (1) What are the theoretical frameworks and areas of study that influence the development of service ecosystems? and (2) To what extent does a service ecosystem align with the theoretical concepts presented in other research contexts within the study areas, thereby transforming the fundamental structure of the core concept?Design/methodology/approachWe conducted a bibliometric systematic literature review, analyzing 280 papers from a sample of 52 journals listed in the Association of Business Schools (ABS). The review covered the period between 2004 and 2022, and we utilized co-citation analysis, multi-dimensional scaling analysis and hierarchical cluster analysis (HCA) on a total of 2,614 citations.FindingsThis study employs co-citation analysis to identify the conceptual structure of the service ecosystem based on highly cited papers. Additionally, we utilize multidimensional scaling (MDS) to uncover key approaches driving service ecosystem research. Through HCA and network analysis, we examine the research scope and its development, emphasizing theory-driven approaches. By combining quantitative and qualitative analysis, we explore the interrelationships between scope, domain and evolution. This comprehensive analysis allows us to delve deeply into the study of service ecosystems. To broaden the research scope, we propose a conceptual framework for comparing the main components of a service ecosystem. The current paper clarifies the service ecosystem's intellectual structure, including service performance, humanistic approach, sustainable innovations and service reflexivity and reformation and proposes a prospective research framework for specialists and researchers by introducing a metaverse service ecosystem.Originality/valueFor the first time, the findings of this study shed light on processes that facilitate the flow of technologies, business models and markets through social structures, ultimately contributing to social change. In service-based systems, the development and application of a more humanistic approach within and surrounding social service ecosystems are crucial as they evolve. Therefore, adopting a dynamic and multifaceted approach offers valuable insights into the drivers of value creation.

Using clusterProfiler to characterize multiomics data.

With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ .

Enhanced Estimation of Traffic Noise Levels Using Minute-Level Traffic Flow Data through Convolutional Neural Network

The advent of high-resolution minute-level traffic flow data from video surveillance on roads has opened up new opportunities for enhancing the estimation of traffic noise levels. In this study, we propose an innovative method that utilizes time series traffic flow data (TSTFD) to estimate traffic noise levels using a deep learning Convolutional Neural Network (CNN). Unlike traditional traffic flow data, TSTFD offer a unique structure and composition suitable for multidimensional data analysis. Our method was evaluated in a pilot study conducted in Foshan City, China, utilizing traffic flow information obtained from roadside video surveillance systems. Our results indicated that the CNN-based model surpassed traditional data-driven statistical models in estimating traffic noise levels, achieving a reduction in mean squared error (MSE) by 10.16%, mean absolute error (MAE) by 4.48%, and an improvement in the coefficient of determination (R²) by 1.73%. The model demonstrated robust generalization capabilities throughout the test period, exhibiting mean errors ranging from 0.790 to 1.007 dBA. However, the model’s applicability is constrained by the acoustic propagation environment, demonstrating effectiveness on roads with similar surroundings while showing limited applicability to those with different surroundings. Overall, this method is cost-effective and offers enhanced accuracy for the estimation of traffic noise level.

Opportunities and challenges of using geospatial technologies in teaching school geography in Kazakhstan

The rapid advancement of technology and widespread access to digital resources have transformed teaching practices and enhanced educators’ competencies. Geographic Information Systems (GIS) offer new avenues for understanding global events and conducting multidimensional analyses. This study aimed to evaluate teachers’ potential use of geospatial technologies by analyzing the objectives of geography curricula for grades 7–11 in Kazakhstan. Additionally, this study investigated barriers to GIS adoption among geography teachers and strategies for cultivating geospatial thinking skills. The research employed curriculum analysis and a teacher survey involving 208 respondents from across Kazakhstan. The results indicated moderate adoption of geospatial technologies, with mobile GIS applications being most prevalent. However, obstacles such as equipment costs, lack of experience, and time constraints hinder their utilization. This study underscores the importance of integrating geospatial technologies in geography education to foster students’ spatial thinking, creativity, and information literacy. Moreover, this study highlights the role of a GIS in enhancing educators’ and students’ proficiency in modern geoinformation systems and digital technologies. The findings offer practical insights for educators and serve as a valuable resource for enhancing geography instruction and promoting geospatial technology integration in the classroom.

Compositional characteristics of the gut microbiome in patients with uremia.

During acute or chronic uremia, the cumulative harmful effects of uremic toxins result in numerous health problems and, ultimately, mortality. Previous research has identified that uremic retention solutes originate from the gut microbiome, indicating that uremia may be closely associated with gut microbiome dysbiosis. To deepen our understanding of the compositional characteristics of the gut microbiome in patients with uremia and thereby promote precision medicine in the treatment of uremia, we conducted a study of the compositional characteristics of the gut microbiome in 20 patients with uremia. The gut microbiome diversity of uremic patients and the control group showed certain differences. Nonmetric multidimensional scaling analysis showed that the beta diversity of the gut microbiome of uremic patients was significantly different from that of the healthy control individuals, with a distinct clustering effect in the uremic patient group, and it also showed a similarly distinct clustering effect in the healthy control group. The Chao1 index and Sobs index were significantly lower in the uremic patient group than in the healthy control group (P < 0.05). By analyzing the composition and abundance distribution of the gut microbiome in the uremic patient group and healthy control group, we found that the relative abundance of the gut microbiome constituents Fusobacteriota, Enterobacteriaceae, Oscillospirales, Ruminococcaceae, and Lachnospiraceae was significantly increased in the intestines of uremic patients. We also detected the rare taxa Erysipelotrichaceae, which was present only in the uremic patient group. Predictive functional analysis suggested that an increased abundance of Ruminococcaceae and Lachnospirales, which are associated with indoxyl sulfate and phenylacetyl glutamine, and an increased abundance of Oscillospirales, which is associated with pyruvate metabolism, in uremic patients may strongly influence the gut environment according to renal function, resulting in dysbiosis associated with uremic toxin production. Rare taxa such as Erysipelotrichaceae have been suggested to be detrimental to intestinal disease. Further research into these gut microbiomes may provide new ideas for the prevention and treatment of uremia with the gut microbiome.

Multi-Dimensional Analysis of Offshore Wind Industry: The Case of French Wind Farms

The development of the offshore wind industry faces a number of organizational and economic hurdles, often strong societal resistance and the instability of public policies, thus drawing interest from academics who have analyzed this process from multiple viewpoints. This body of knowledge has served as the basis for a study aiming at characterizing France’s first three offshore wind farm projects, which finally reached the energy production stage after over ten years of development. Results show that although this development has been the subject of atypically strong social opposition, the projects’ features are quite close to those highlighted by the literature.

A bibliometric and visualization analysis of global research status and frontiers on autophagy in cardiomyopathies from 2004 to 2023.

Autophagy is intimately associated with the development of cardiomyopathy, and has received widespread attention in recent years. However, no relevant bibliometric analysis is reported at present. In order to summarize the research status of autophagy in cardiomyopathy and provide direction for future research, we conducted a comprehensive, detailed, and multidimensional bibliometric analysis of the literature published in this field from 2004 to 2023. All literatures related autophagy in cardiomyopathy from 2004 to 2023 were collected from the Web of Science Core Collection (WOSCC), and annual papers, global publication trends and proportion charts were analyzed and plotted using Graphpad price v8.0.2. In addition, CtieSpace (6.2.4R (64 bit) Advanced Edition) and VOSviewer (1.6.18 Edition) were used to analyze and visualize these data. 2279 papers about autophagy in cardiomyopathy were accessed in the WoSCC over the last 20 years, comprising literatures from 70 countries and regions, 2208 institutions, and 10,810 authors. China contributes 56.32% of the total publications, substantially surpassing other countries, while the U.S. is ranked first in frequency of citations. Among the top 10 authors, 6 are from China and 4 are from the United States. Air Force Military Medical University was the institution with the highest number of publications; while journal of molecular and cellular cardiology (62 articles, 2.71% of the total) was the journal with the highest number of papers published in the field. Clustering of co-cited references and temporal clustering analysis showed that ferroptosis, hydrogen sulfide mitophagy, lipid peroxidation, oxidative stress, and SIRT-1 are hot topics and trends in the field. The principal keywords are oxidative stress, heart and heart-failure. The research on autophagy in cardiomyopathy is in the developmental stage. This represents the first bibliometric analysis of autophagy in cardiomyopathy , revealing the current research hotspots and future research directions in this field.

Exploring the impact of negative online feedback on well-being: A comprehensive analysis incorporating Big-Five personality traits and physiological responses

This study investigates the dynamics between social media engagement and well-being, employing a multi-dimensional analysis encompassing psychological, behavioral, social media usage patterns, and physiological perspectives. Through the utilization of electrodermal activity, our study identified a statistically significant increase following exposure to a negative comment. In addition, analysis of facial expressions revealed a variety of reactions, including laughter, irritation, sadness, and neutrality, once participants were confronted with the negative comment. Our analysis revealed intriguing insights into the relationship between personality traits and social media interactions. We observed that individuals with higher agreeableness were more likely to contemplate deactivating social media platforms. Conversely, lower levels of neuroticism were associated with self-reported social media addiction, while extraversion showed a positive correlation with increased posting frequency. These findings highlight the intricate relationship between individual personality traits and reactions to negative social media experiences, offering valuable insights into the complex dynamics of online interactions and their effects on overall well-being.

Synergy Between NK Cells and Monocytes in Potentiating Cardiovascular Disease Risk in Severe COVID-19.

Evidence suggests that COVID-19 predisposes to cardiovascular diseases (CVDs). While monocytes/macrophages play a central role in the immunopathogenesis of atherosclerosis, less is known about their immunopathogenic mechanisms that lead to CVDs during COVID-19. Natural killer (NK) cells, which play an intermediary role during pathologies like atherosclerosis, are dysregulated during COVID-19. Here, we sought to investigate altered immune cells and their associations with CVD risk during severe COVID-19. We measured plasma biomarkers of CVDs and determined phenotypes of circulating immune subsets using spectral flow cytometry. We compared these between patients with severe COVID-19 (severe, n=31), those who recovered from severe COVID-19 (recovered, n=29), and SARS-CoV-2-uninfected controls (controls, n=17). In vivo observations were supported using in vitro assays to highlight possible mechanistic links between dysregulated immune subsets and biomarkers during and after COVID-19. We performed multidimensional analyses of published single-cell transcriptome data of monocytes and NK cells during severe COVID-19 to substantiate in vivo findings. During severe COVID-19, we observed alterations in cardiometabolic biomarkers including oxidized-low-density lipoprotein, which showed decreased levels in severe and recovered groups. Severe patients exhibited dysregulated monocyte subsets, including increased frequencies of proinflammatory intermediate monocytes (also observed in the recovered) and decreased nonclassical monocytes. All identified NK-cell subsets in the severe COVID-19 group displayed increased expression of activation and tissue-resident markers, such as CD69. We observed significant correlations between altered immune subsets and plasma oxidized-low-density lipoprotein levels. In vitro assays revealed increased uptake of oxidized-low-density lipoprotein into monocyte-derived macrophages in the presence of NK cells activated by plasma of patients with severe COVID-19. Transcriptome analyses confirmed enriched proinflammatory responses and lipid dysregulation associated with epigenetic modifications in monocytes and NK cells during severe COVID-19. Our study provides new insights into the involvement of monocytes and NK cells in the increased CVD risk observed during and after COVID-19.

Determining the relationship between oral health and physical function in elderly people.

Poor oral conditions in the elderly may have numerous effects on general health, including physical fitness and performance. This study aimed to determine the relationship between oral health and physical function in elderly people. Physical function and oral health parameters were compared using parametric comparison tests and Pearson correlation analyses. In addition, principal components analysis, hierarchical clustering and multidimensional scaling analysis clustered the patients' physical and oral health scores. The relationship between the groups was also determined using decision tree analysis. A total of 112 elderly patients participated in the study. Grip strength (GS) was higher in patients with high chewing ability, and Timed Up and Go (TUG) scores were lower in the high oral health group (p < .05). GS was correlated with Decay, Missing, and Filled Teeth Index (DMFT) and the number of remaining and functional teeth (p < .05). According to principal component analysis, it was seen that there were three components (oral, functional and quality of life (QoL) parameters), and the features that were related to each other were gathered together. TUG and GS showed the highest relative importance among physical function criteria in the classification based on chewing ability. They were GS and physical activity for oral health-related QoL. In the elderly, higher physical function parameters, especially GS may be an indicator of a better oral health and oral health-related QoL. Preventive physical rehabilitation practices, in addition to oral treatments, may be effective in improving oral health in the elderly.

Taxonomic and Functional Diversity of Reptiles in a Heterogeneous Landscape of Jalisco State, West-Central Mexico

Reptiles are threatened by anthropogenic landscape transformation, largely due to agriculture. However, ecosystems nowadays constitute a matrix of fragmented landscapes. We analyzed a heterogeneous landscape’s reptile taxonomic and functional diversity patterns across ten land cover/use types in Jalisco state, in west-central Mexico. At the alpha diversity level, we assessed the taxonomic diversity using q-order indices, and functional diversity with multidimensional indices (FRic, FDiv, and FEve) by land cover/use. We evaluated the differences in species composition among land cover/use types (beta diversity). We utilized multidimensional distance-based analyses (dbRDA) to evaluate the association between reptile diversity, habitat structure, and environmental variables. Species richness did not correlate with functional richness across land cover types overall, except for riparian habitat surrounded by crops (RH-C), which exhibited higher species richness and functional diversity. Secondary vegetation surrounded by temperate forest (SV-TF), riparian habitat surrounded by tropical dry forest (RH-TDF), and RH-C were the land cover/use types with the most functional groups. Herbaceous cover is crucial for preserving both reptile diversity facets in this landscape. These findings suggest that the availability of resources (e.g., riparian habitat and herbaceous cover) regardless of perturbation level could be more relevant for reptile diversity than the condition (tropical vs. temperate) due to the high plasticity and adaptation of the group. It is essential to recognize the ecological value of these habitats by adopting a holistic approach that values the intrinsic and ecological importance of reptile diversity.

Revealing microbial community characteristics in healthy human, cat and canine salivas and looking for species-specific microbes.

As two kinds of increasingly popular pets, the saliva of cat or canine is most likely to be left at the crime scene compared with the common types of body fluids in forensics. Accurately identifying the species of saliva samples found at the crime scene involving pets will help the investigators find available testing materials, reduce the consumption of reagents and save the investigative time of the case. Therefore, it is necessary to explore the characteristics and differences of saliva microbiomes of cat, canine and human. In this study, 16S rRNA gene amplicon sequencing technology was used to reveal microbial communities of saliva samples of healthy human, cat, and canine. Alpha diversity analyses indicated that canine saliva demonstrated the highest microbial diversity, followed by cat saliva, whereas human saliva microbial diversity was the lowest. The saliva samples of the three species all had their own unique microbial community compositions, and the dominant phyla of canine and cat salivas were Proteobacteria and Bacteroidete, while the dominant phyla of human saliva were Firmicutes and Proteobacteria. There was no significant statistical difference in the salivary microbiota obtained by the two collection methods (cotton swab and liquid saliva). The gender of cats and canines might have no effect on the salivary microbiota, but the different breeds had an impact on their saliva microbiomes. Principal coordinates analysis, non-metric multidimensional scaling analysis and random forest analysis all indicated significant differences in microbial community structures among the three species, allowing inference on the species sources of saliva samples by microbiome method. Differential microbial biomarkers for the salivas of three species were screened out using a variety of bioinformatics analyses, and the results demonstrated that Prevotella melaninogenica, Veillonella parvula, and Haemophilus parainfluenzae could be used as species-specific microbial biomarkers of human saliva. The detections of human species-specific microbes provide a potential method for determining human saliva.

Revealing microbial community characteristics in healthy human, cat and canine salivas and looking for species-specific microbes.

As two kinds of increasingly popular pets, the saliva of cat or canine is most likely to be left at the crime scene compared with the common types of body fluids in forensics. Accurately identifying the species of saliva samples found at the crime scene involving pets will help the investigators find available testing materials, reduce the consumption of reagents and save the investigative time of the case. Therefore, it is necessary to explore the characteristics and differences of saliva microbiomes of cat, canine and human. In this study, 16S rRNA gene amplicon sequencing technology was used to reveal microbial communities of saliva samples of healthy human, cat, and canine. Alpha diversity analyses indicated that canine saliva demonstrated the highest microbial diversity, followed by cat saliva, whereas human saliva microbial diversity was the lowest. The saliva samples of the three species all had their own unique microbial community compositions, and the dominant phyla of canine and cat salivas were Proteobacteria and Bacteroidete, while the dominant phyla of human saliva were Firmicutes and Proteobacteria. There was no significant statistical difference in the salivary microbiota obtained by the two collection methods (cotton swab and liquid saliva). The gender of cats and canines might have no effect on the salivary microbiota, but the different breeds had an impact on their saliva microbiomes. Principal coordinates analysis, non-metric multidimensional scaling analysis and random forest analysis all indicated significant differences in microbial community structures among the three species, allowing inference on the species sources of saliva samples by microbiome method. Differential microbial biomarkers for the salivas of three species were screened out using a variety of bioinformatics analyses, and the results demonstrated that Prevotella melaninogenica, Veillonella parvula, and Haemophilus parainfluenzae could be used as species-specific microbial biomarkers of human saliva. The detections of human species-specific microbes provide a potential method for determining human saliva.

A morphological study on the question of the hybrid origin of Astrophytum coahuilense (Cactaceae)

Two alternative hypotheses may explain the origin of Astrophytum coahuilense. One hypothesis is that A. coahuilense is an ancient diploid hybrid derived from interbreeding between A. capricorne and A. myriostigma. An alternative is that A. coahuilense arose via allopatric speciation and its similarity to A. myriostigma in vegetative characters is due to convergent evolution. The two hypotheses are tested, applying univariate analysis and Non-metric Multidimensional Scaling analysis to morphological datasets. Univariate analysis, which is based on 13 vegetative and 10 reproductive characters, shows that the morphology of A. coahuilense is complex, consisting of a combination of vegetative and reproductive characters shared with both A. capricorne and A. myriostigma as well as several intermediate traits. Furthermore, the morphology of A. coahuilense lacks unique, fixed characteristics. Non-metric Multidimensional Scaling analysis of 15 characters (three vegetative and 12 reproductive traits) produced a two-dimensional scatterplot in which the cluster representing A. coahuilense occupies a position intermediate between the clusters of the two parental species along Dimension 1. Thus, both analyses support the hypothesis that Astrophytum coahuilense originated through hybridization between A. capricorne and A. myriostigma. Biological and ecological factors that may have facilitated homoploid hybrid speciation are discussed.

A morphological study on the question of the hybrid origin of Astrophytum coahuilense (Cactaceae)

Two alternative hypotheses may explain the origin of Astrophytum coahuilense. One hypothesis is that A. coahuilense is an ancient diploid hybrid derived from interbreeding between A. capricorne and A. myriostigma. An alternative is that A. coahuilense arose via allopatric speciation and its similarity to A. myriostigma in vegetative characters is due to convergent evolution. The two hypotheses are tested, applying univariate analysis and Non-metric Multidimensional Scaling analysis to morphological datasets. Univariate analysis, which is based on 13 vegetative and 10 reproductive characters, shows that the morphology of A. coahuilense is complex, consisting of a combination of vegetative and reproductive characters shared with both A. capricorne and A. myriostigma as well as several intermediate traits. Furthermore, the morphology of A. coahuilense lacks unique, fixed characteristics. Non-metric Multidimensional Scaling analysis of 15 characters (three vegetative and 12 reproductive traits) produced a two-dimensional scatterplot in which the cluster representing A. coahuilense occupies a position intermediate between the clusters of the two parental species along Dimension 1. Thus, both analyses support the hypothesis that Astrophytum coahuilense originated through hybridization between A. capricorne and A. myriostigma. Biological and ecological factors that may have facilitated homoploid hybrid speciation are discussed.

Can China's energy policies achieve the "dual carbon" goal? A multi-dimensional analysis based on policy text tools

AbstractThe continuous increase in global temperatures and frequency of extreme weather events underscore the urgency of achieving "dual carbon" goals. Systematically examining the textual characteristics of energy policies under the "dual carbon" framework, synthesizing the implementation pathways of "dual carbon" initiatives contribute to enhancing comprehension, execution, and optimisation of these policies. This study selecting 409 energy policies under the "dual carbon" goals issued by the central government, the State Council, and ministries from 1992 to 2022 as samples, using policy econometrics and content analysis methods, external characteristics of energy policies including publication time, publication type, and publication subject were analysed. Multidimensional content analysis was conducted for policy tools, objectives, and participants. The implementation path of the "dual carbon" goals was summarised. The study found that China's energy policy under "dual carbon" target has undergone four development stages before and after the release of the energy policy, and energy policy system has been continuously supplemented and improved. Various energy policy tools, diverse policy participants, and policy objectives are of concern and focus. From the "tool-subject-target" perspective, six paths for the implementation of "double carbon" targets in China's energy field are summarised. The energy policy under the "dual carbon" goal has problems such as lack of laws and regulations, over-use of environmental policy tools, uneven distribution in the internal segmentation of policy tools, insufficient involvement of market players and the public, and insufficient attention to the reform of energy systems and mechanisms and the goal of establishing energy-saving and low-carbon concepts.