Clustering Pattern Research Articles

Construction of Landscape Ecological Risk Collaborative Management Network in Mountainous Cities—A Case Study of Zhangjiakou

The prevention of ecological risks is a critical determinant influencing sustainable development. Driven by rapid socio-economic development, the ecosystems of mountainous cities within agro-pastoral transition zones are increasingly vulnerable to complex disturbances, constituting a significant threat to sustainable development and human well-being. To help achieve sustainable development, it is essential to conduct research on addressing and mitigating ecological risks from the perspective of collaborative management networks in mountainous cities. Taking Zhangjiakou as the study area, this paper employed the land use transfer matrix and standard deviation ellipse methods to analyze the dynamic land use changes. Additionally, using Fragstats 4,2 to calculate the landscape indices with land use data, this paper evaluated the landscape ecological risk (LER) from 2000 to 2020. Furthermore, the social network analysis (SNA) method was utilized to explore the spatial correlation characteristics of the LER. The findings indicate that: (1) Cultivated land and grassland were the predominant land use types in Zhangjiakou. During 2000–2020, Zhangjiakou experienced significant changes in land use, dominated by the transfer among cultivated land, forestland, and grassland. It indicated that the issue of unstable ecological land use continued to exist. Affected by human activities, construction land showed a consistent upward trend, primarily concentrated in the urban built-up areas and areas along the Jing-Zhang Railway. (2) The LER of Zhangjiakou was predominantly characterized by low risk, medium risk, and high risk levels. In the transitional areas and foothills, the LER was relatively higher. During 2000–2020, Zhangjiakou showed a declining trend of LER. This suggested that the ecological protection policies in Zhangjiakou were effective, leading to an improvement in the local ecological environment. (3) The LER in Zhangjiakou demonstrated a spatial clustering pattern that exhibited an upward trend, which was supported by both spatial autocorrelation and the SNA analysis. In the LER collaborative management network, Xuanhua, Qiaodong, Qiaoxi, Wanquan and Zhangbei consistently upheld pivotal roles. Based on the number of inward and outward connections, 16 counties in Zhangjiakou were classified into four categories and three zones accompanied by corresponding recommendations. The findings of this study can serve as a valuable reference for subsequent landscape pattern optimization and ecological restoration in Zhangjiakou.

Mugen-UMAP: UMAP visualization and clustering of mutated genes in single-cell DNA sequencing data

BackgroundThe application of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and visualization has revolutionized the analysis of single-cell RNA expression and population genetics. However, its potential in single-cell DNA sequencing data analysis, particularly for visualizing gene mutation information, has not been fully explored.ResultsWe introduce Mugen-UMAP, a novel Python-based program that extends UMAP’s utility to single-cell DNA sequencing data. This innovative tool provides a comprehensive pipeline for processing gene annotation files of single-cell somatic single-nucleotide variants and metadata to the visualization of UMAP projections for identifying clusters, along with various statistical analyses. Employing Mugen-UMAP, we analyzed whole-exome sequencing data from 365 single-cell samples across 12 non-small cell lung cancer (NSCLC) patients, revealing distinct clusters associated with histological subtypes of NSCLC. Moreover, to demonstrate the general utility of Mugen-UMAP, we applied the program to 9 additional single-cell WES datasets from various cancer types, uncovering interesting patterns of cell clusters that warrant further investigation. In summary, Mugen-UMAP provides a quick and effective visualization method to uncover cell cluster patterns based on the gene mutation information from single-cell DNA sequencing data.ConclusionsThe application of Mugen-UMAP demonstrates its capacity to provide valuable insights into the visualization and interpretation of single-cell DNA sequencing data. Mugen-UMAP can be found at https://github.com/tengchn/Mugen-UMAP

Unveiling Fusaria mycoflora associated with natural occurrence of lisianthus wilt and stem rot in Central-highland Vietnam.

Wilt and stem rot (WSR) is an emerging syndrome threatening cut lisianthus (Eustoma russellianum) production in Lam Dong province, Vietnam. The disease was observed in all 13 inspected commercial lisianthus greenhouses across major lisianthus cultivation areas in Lam Dong, including Da Lat, Lac Duong, Don Duong, and Duc Trong, with incidence increasing with plant age, ranging from 7.5 to 32.4%. Infected plants displayed stunting, wilting, stem rot and blight, and dieback, with predominance of wilt and stem rot. The disease showed polycyclic behavior, with symptoms shifting from random or scattered in young plants to clustered patterns after the initial flower cutting. Forty-one Fusaria-like fungal isolates recovered from diseased lisianthus plants were identified as Fusarium vanleeuwenii (28 isolates), Neocosmospora solani (11 isolates), and F. annulatum (2 isolates) based on morphological observations and phylogenetic analysis of the internal transcribed spacer (ITS) region and translation elongation factor 1-alpha (TEF-1α) genes. The composition of Fusaria species varied across sites, with F. vanleeuwenii being consistently present. Pathogenicity tests confirmed that isolates of F. vanleeuwenii Li-Fo9511, N. solani Li-Fs4311, and F. annulatum Li-Fp3051 caused typical stem rot in in-vitro assays. In-planta assays showed wilting in seedlings starting two weeks post-infection, with a remarkable increase in disease incidence and severity between five and six weeks, particularly for F. vanleeuwenii Li-Fo9511. The pathogens were re-isolated and morphologically confirmed, fulfilling Koch's postulates. This is the first report of F. vanleeuwenii, N. solani, and F. annulatum as pathogens of lisianthus WSR in Vietnam, highlighting the need for effective control strategies.

Spatial distribution characteristics of Tibetan Buddhism principal-subordinate monastery systems in the Hehuang region

Tibetan Buddhist monasteries constitute significant cultural heritages of ethnic minorities, evolving into the Principal-Subordinate Monastery System (PSMS) with profound historical and cultural significance. This system exhibits an integrated hierarchical structure in terms of religious dominance, political administration, and cultural diffusion. Existing research primarily focuses on internal humanistic factors such as governance patterns, while there is not any research focused on its patterns and manifestations in spatial distribution. This study aims to elucidate the spatial distribution characteristics of the PSMS in Hehuang region, including the Tibetan Buddhist monasteries from all sects in the Hehuang region since the Song Dynasty It establishes a hierarchical PSMS database based on the affiliation relationships, aimed at storing spatial and property information related to PSMS. The database standardizes the naming and coding of monasteries, and classifies them through hierarchical relationships to ensure data consistency and usability. Finally, the classified and coded monastery data were analyzed by GIS tools to form the PSMS spatial distribution characterisation framework. Results reveal that (1) Monasteries demonstrate notable spatial clustering patterns from both holistic and sectarian perspectives, with density being influenced by the principal monastery’s position. (2) Almost every PSMS exhibits a spatial pattern centered around the principal monastery, with subordinate monasteries clustering within a defined range correlated with the administrative region of the principal monastery. (3) PSMS scale variations are controlled by principal monasteries, exhibiting positive correlations in hierarchical structure, control quantity, and distribution range. (4) The spatial orientation of PSMS correlates with river distribution, while the relationships among mountain ranges require further investigation. These findings provide initial insights into the spatial distribution characteristics of PSMS, confirming the spatial influence of the principal monastery. Besides, this study established an innovative spatial research framework for heritage clusters with multiple types and hierarchies. Thus, this study offers new insights into the spatial distribution of Tibetan Buddhist monastery heritage and presents a framework for further examining the spatial distribution of the Tibetan Buddhist monastery heritage in other regions, as well as other heritage clusters where connected and clustered through religious and cultural ties.

Oral diseases and systemic conditions: correlation analyses from the Colombian national health records between 2016 and 2023.

Prevalence of oral, metabolic, circulatory, and nutritional diseases has tended to increase globally. As these diseases have common contributing factors, such as unhealthy diets, evaluating their epidemiological trends and the relation between them is relevant. In Colombia, the Ministry of Health records the frequency of consultation for these diseases through the Integrated Social Protection Information System. It facilitates the evaluation of their epidemiological behavior at the population level. To analyze and correlate the burden of oral diseases (caries and periodontitis) with respect to nutritional, circulatory and metabolic pathologies, as well as their relationships with socioeconomic indices in Colombian regions from 2016 to 2023. This study analyzes retrospective data collected between 2016 and 2023 by the National Health Registry in Colombia (SISPRO) according to the ICD-10. Sociodemographic data and the number of disease consultations were extracted. The number of consultations for oral diseases was correlated with systemic pathologies, socioeconomic indices through the Spearman test. Associations were explored via multiple linear regressions. A Principal Component Analyses (PCA) was conducted to identify patterns between socioeconomic, oral and systemic disease variables, as well as to reduce the complexity of the analysis by creating clusters that capture the greatest possible variability in the data. The average number of consultations per biennium was 24.550.435 being the highest number for 2018-2019. The highest percentage of consultations was related to extensive caries, followed by chronic gingivitis. Moreover, consultations for oral diseases were found to correlate with systemic pathologies. All socioeconomic indices were associated with caries and/or periodontal diseases. This study is the first in Colombia that uses national data on diagnoses assigned to medical and dental consultations. PCA revealed a clustering pattern of pathologies suggesting that they are highly associated one to each other. Correlations between consultations for oral and systemic diseases stratified by life cycle and socioeconomic indices highlight the importance of conducting epidemiological and public health characterizations and their associations. Identifying these factors is imperative in the prevention and management of both diseases.

Epidemiological Characteristics and Spatiotemporal Clustering of Pulmonary Tuberculosis Among Students in Southwest China From 2016 to 2022: Analysis of Population-Based Surveillance Data.

Pulmonary tuberculosis (PTB), as a respiratory infectious disease, poses significant risks of covert transmission and dissemination. The high aggregation and close contact among students in Chinese schools exacerbate the transmission risk of PTB outbreaks. This study investigated the epidemiological characteristics, geographic distribution, and spatiotemporal evolution of student PTB in Chongqing, Southwest China, aiming to delineate the incidence risks and clustering patterns of PTB among students. PTB case data from students monitored and reported in the Tuberculosis Information Management System within the China Information System for Disease Control and Prevention were used for this study. Descriptive analyses were conducted to characterize the epidemiological features of student PTB. Spatial trend surface analysis, global and local spatial autocorrelation analyses, and disease rate mapping were performed using ArcGIS 10.3. SaTScan 9.6 software was used to identify spatiotemporal clusters of PTB cases. From 2016 to 2022, a total of 9920 student TB cases were reported in Chongqing, Southwest China, with an average incidence rate of 24.89/100,000. The incidence of student TB showed an initial increase followed by a decline, yet it remained relatively high. High school students (age: 13-18 years; 6649/9920, 67.03%) and college students (age: ≥19 years; 2921/9920, 29.45%) accounted for the majority of student PTB cases. Patient identification primarily relied on passive detection, with a high proportion of delayed diagnosis and positive etiological results. COVID-19 prevention measures have had some impact on reducing incidence levels, but the primary factor appears to be the implementation of screening measures, which facilitated earlier case detection. Global spatial autocorrelation analysis indicated Moran I values of >0 for all years except 2018, ranging from 0.1908 to 0.4645 (all P values were <.05), suggesting strong positive spatial clustering of student PTB cases across Chongqing. Local spatial autocorrelation identified 7 high-high clusters, 13 low-low clusters, 5 high-low clusters, and 4 low-high clusters. High-high clusters were predominantly located in the southeast and northeast parts of Chongqing, consistent with spatial trend surface analysis and spatiotemporal clustering results. Spatiotemporal scan analysis revealed 4 statistically significant spatiotemporal clusters, with the most likely cluster in the southeast (relative risk [RR]=2.87, log likelihood ratio [LLR]=574.29, P<.001) and a secondary cluster in the northeast (RR=1.99, LLR=234.67, P<.001), indicating higher reported student TB cases and elevated risks of epidemic spread within these regions. Future efforts should comprehensively enhance prevention and control measures in high-risk areas of PTB in Chongqing to mitigate the incidence risk among students. Additionally, implementing proactive screening strategies and enhancing screening measures are crucial for early identification of student patients to prevent PTB outbreaks in schools.

UPLC‐MS/MS and chemometrics analyses reveal chemical profile and anti‐inflammatory activity of Bienertia cycloptera Bunge fractions

AbstractIntroductionBienertia cycloptera is a species belonging to the Chenopodiaceae family. According to earlier reports, a unique research study on the phytochemistry and biological analysis of that species was conducted.ObjectiveThis study presents an integrated metabolomics investigation combined with multivariate analysis of various extractive fractions of B. cycloptera aerial parts. This study is the first attempt to explore the anti‐inflammatory metabolites from B. cycloptera, showing its significance as a valuable traditional medicine.MethodologyBy comparing retention times, quasi‐molecular ions, and MS/MS fragment ions with databases and literature references, metabolite annotation was accomplished using ultra performance liquid chromatography (UPLC)/triple quadrupole mass spectrometry (MS). Moreover, the effects of the studied samples on the gene expression of the four pro‐inflammatory cytokines (IL‐1β, IL‐6, TNF‐α, and INF‐γ) using polymerase chain reaction (PCR) and comparing their results by those caused by piroxicam were tested to determine their anti‐inflammatory efficacy.ResultsChemical profiling revealed diverse metabolites, with 62 chromatographic peaks identified across various chemical classes. UPLC‐MS/MS of different B. cycloptera fractions unveiled distinct chemical profiles. Results showed distinct chemical compositions in each fraction, with petroleum ether fraction enriched in sterols and fatty acids; methylene chloride fraction in alkaloids, sterols, and cardenolides; ethyl acetate fraction in alkaloids, flavonoids, cardenolides, and phenolic acids; and n‐butanol fraction in flavonoids, alkaloids, and phenolic acids. Multivariate data analysis illustrated clustering patterns among petroleum ether, methylene chloride, ethyl acetate, and n‐butanol fractions. OPLS‐DA models were constructed to discern inter‐class differences, identifying discriminatory metabolites. In vitro cytotoxicity and anti‐inflammatory assays demonstrated the safety and efficacy of B. cycloptera fractions, with significant downregulation of pro‐inflammatory markers. Further analysis revealed specific metabolites associated with anti‐inflammatory effects, such as p‐hydroxybenzoic acid, vanillic acid, tachioside, ferulic acid, staphylionoside D, humilixanthin, bergaptol, vulgaxanthin I, and portulacaxanthin III.ConclusionThe findings of this study provide valuable insights into the chemical composition and bioactivity of B. cycloptera fractions, suggesting their potential as therapeutic agents and warranting further investigation.

Resilience among older adults with multimorbidity using the Connor-Davidson scale in the Canadian Longitudinal Study on Aging: health behaviour, socio-economic, and social support predictors

ObjectiveMultimorbidity is recognized as a serious health condition faced by a majority of older adults. Research investigating adaptive responses to multimorbidity, termed multimorbidity resilience, has been growing. This paper examines protective and risk factors, with a focus on health behaviours, socio-economic resources, and social support using an established measure of resilience (Connor-Davidson Resilience Scale) among older adults, focusing on older persons with two or more concurrent chronic conditions.MethodsUsing Baseline (2011–2015), Follow-up One (2015–2018), and Follow-up Two (2018–2021) data from the Comprehensive Cohort of the Canadian Longitudinal Study on Aging, we tested hypotheses using 13,064 participants aged 65 years and older, who completed all waves and reported two or more of 27 chronic conditions, for the full sample of multimorbid individuals and three multimorbidity clusters: Cardiovascular/Metabolic, Musculoskeletal, and Mental Health. Associations between protective and risk factors and resilience were examined using linear regression to model the Connor-Davidson resilience scale, adjusting for illness context and social determinants of health.ResultsAmong all multimorbid individuals, the strongest associations with resilience were found for higher self-rated health, greater sleep satisfaction, better appetite, higher household income, more relatives and friends, being overweight (compared to normal weight), fewer housing problems, and fewer skipped meals. Weaker associations were found for non-smokers, less alcohol consumption, less pain, sedentary behaviour, being non-married (compared to married), and among Canadian born (compared to foreign). The analyses for the three multimorbidity clusters were largely replicated for the three multimorbidity clusters, but with some nuances depending on the cluster.DiscussionThis research provides confirmatory evidence for several protective and risk factors affecting the ability to cope and recover from multimorbidity adversity among older adults. There are consistent patterns for the multimorbidity disease clusters, but some distinct relationships arise that are worthy of attention. The implications of the findings for modifiable health behaviours and socio-economic factors are discussed for their public health and clinical relevance.

Gene-level alignment of single-cell trajectories

AbstractSingle-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.

Understanding the Emergence of Comorbidity between Problematic Online Gaming and Gambling: A Network Analysis Approach.

Problematic online gaming and gambling tend to co-occur. The exact mechanisms underlying this phenomenon and the potential effects of gender differences remain unknown. This study aimed to identify the early clustering patterns of problematic online gaming and gambling in a community sample of young adults without a lifetime history of psychiatric treatment. Data were collected through an online survey and analyzed using partial correlations and Bayesian networks. Altogether, 1441 individuals (aged 18-40 years, 51.4% females) participated in the survey. Both problematic online behaviors were weakly interrelated, suggesting that they serve as distinct constructs. Men's networks appeared to be more complex and had significantly higher global connectivity. Moreover, men and women differed with respect to the specific nodes that bridged both constructs. In men, the bridge nodes were "being criticized because of betting or being told about gambling problems", "loss of previous interests due to gaming", "deceiving other people because of gaming", and "health consequences of gambling". Among women, the bridge nodes were "feeling guilty because of gambling", "loss of previous interests because of gaming", "social consequences of gaming", and "continued gaming problems with other people". In men, the strongest edge was found between "borrowing money/selling anything to gamble" and "financial problems because of gambling", while in women, the strongest edge appeared between "betting more than afforded to be lost" and "tolerance symptoms of gambling". The findings indicate that problematic online gaming and gambling tend to emerge in different ways among men and women. Therapeutic interventions should be planned considering gender differences.

Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan

While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with health disparities remain poorly explored and thus require deep understanding within a regional context. Furthermore, disregarding the importance of spatial spillover effects and nonlinear effects of covariates on childhood stunting are inevitable in dealing with an enduring issue of regional health inequalities. The present study aims to investigate the spatial inequalities in childhood stunting at the district level in Pakistan and validate the importance of spatial lag in predicting childhood stunting. Furthermore, it examines the presence of any nonlinear relationships among the selected independent features with childhood stunting. The study utilized data related to socioeconomic features from MICS 2017–2018 and climatic data from Integrated Contextual Analysis. A multi-model approach was employed to address the research questions, which included Ordinary Least Squares Regression (OLS), various Spatial Models, Machine Learning Algorithms and Explainable Artificial Intelligence methods. Firstly, OLS was used to analyse and test the linear relationships among selected variables. Secondly, Spatial Durbin Error Model (SDEM) was used to detect and capture the impact of spatial spillover on childhood stunting. Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. Finally, EXAI methods such as SHapley were utilized to identify potential nonlinear relationships. The study found a clear pattern of spatial clustering and geographical disparities in childhood stunting, with multidimensional poverty, high climate vulnerability and early marriage worsening childhood stunting. In contrast, low climate vulnerability, high exposure to mass media and high women’s literacy were found to reduce childhood stunting. The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. Furthermore, EXAI methods such as partial dependency plot reveal the existence of a nonlinear relationship between multidimensional poverty and childhood stunting. The study’s findings provide valuable insights into the spatial distribution of childhood stunting in Pakistan, emphasizing the importance of considering spatial effects in predicting childhood stunting. Individual and household-level factors such as exposure to mass media and women’s literacy have shown positive implications for childhood stunting. It further provides a justification for the usage of EXAI methods to draw better insights and propose customised intervention policies accordingly.

A Nomogram Based on Features of Ultrasonography and Contrast-Enhanced CT to Predict Vessels Encapsulating Tumor Clusters Pattern of Hepatocellular Carcinoma

ObjectiveThis study aimed to establish a clinical prediction model for vessels encapsulating tumor clusters (VETC) based on preoperative ultrasonography (US) and contrast-enhanced computed tomography (CECT) imaging in patients with hepatocellular carcinoma (HCC). MethodsData were retrospectively collected from 215 patients who underwent hepatectomy for solitary HCC lesions. They were divided into training and validation cohorts at a ratio of 6:4. Preoperative imaging features were extracted (seven from US and nine from CECT imaging) to explore their relationship with VETC. A VETC prediction model was constructed and graphically depicted as a nomogram. Its performance was evaluated via the receiver operating characteristic (ROC) curve, the calibration curve, and decision curve analysis (DCA). ResultsThe VETC incidence for all the lesions was 37.7%. The final variables included in the nomogram were “peritumoral enhancement in CECT”, “alpha-fetoprotein level > 200 ng/Ml,” “halo in US,” “capsule enhancement in CECT,” and “posterior acoustic enhancement in US.” The area under the curve (AUC) values for the training and validation cohorts were 0.824 and 0.725, respectively. The Hosmer–Lemeshow fit test showed no statistical difference (p = 0.369 and p = 0.067 for the training and validation cohorts, respectively). DCA demonstrated that our nomogram provided clinical benefits to a wide range of patients. According to the nomogram score, the VETC-positive and -negative groups demonstrated significant differences in both the training (p < 0.001) and validation (p = 0.001) cohorts. ConclusionOur prediction model based on US and CECT imaging features can accurately predict VETC in HCC.

Spatial-temporal evolution patterns of influenza incidence in Xinjiang Prefecture from 2014 to 2023 based on GIS

Using GIS technology, this study investigated the spatiotemporal distribution pattern of influenza incidence in Xinjiang from 2014 to 2023 based on influenza surveillance data. The study revealed a noticeable fluctuation trend in influenza incidence rates in Xinjiang, particularly notable spikes observed in 2019 and 2023. The results of the 3-year moving average showed a significant long-term upward trend in influenza incidence rates, confirmed by Theil-Sen method (MAD = 2.202, p < 0.01). Global spatial autocorrelation analysis indicated significant positive spatial autocorrelation in influenza incidence rates from 2016 and from 2018 to 2023 (Moran’s I > 0, P < 0.05). Local spatial autocorrelation analysis further revealed clustering patterns in different regions, with high-high clustering and low-high clustering predominating in northern Xinjiang, and low-low clustering predominating in southern Xinjiang. Hotspot analysis indicated a progressive rise in the number of influenza incidence hotspots, primarily concentrated in northern Xinjiang, particularly in Urumqi, Ili Kazakh Autonomous Prefecture, and Hotan Prefecture. Standard deviation ellipse analysis and the trajectory of influenza incidence gravity center migration showed that the transmission range of influenza in Xinjiang has been expanding, with the epidemic center gradually moving northward. The spatiotemporal heterogeneity of influenza incidence in Xinjiang highlights the need for differentiated and precise influenza prevention and control strategies in different regions to address the changing trends in influenza prevalence.

Identifying the determinants of E-commerce offerings among grocery store locations in the United States: A spatial econometric analysis

This paper leverages store location data and analysis software from retail industry vendors to analyze the unique geographies of electronic commerce (e-commerce) offerings among major grocery chains in the United States. Join-Count statistics are calculated to identify patterns of clustering among offerings, and a spatial probit model is implemented to regress various trade area characteristics on a given grocery banner's decision to offer some form of e-commerce (pickup, delivery, or both) or no offering at a given store location. A regional case study of the Southeastern United States demonstrates how model results can shift based on scale, pointing to unique regional dynamics that deviate from broader national trends. This analysis carries implications for location planning and competitive defense strategies in the grocery industry, and also establishes avenues for future research.

Interconnectedness and return spillover among APEC currency exchange rates: A time-frequency analysis

By combining TVP-VAR Model (time domain connectedness) and TVP-VAR based Baruník and Křehlík model (frequency domain connectedness), this study analyzes the impact of the COVID-19 pandemic, the Russia-Ukraine war, and the Silicon Valley Bank (SVB) collapse on the Asia Pacific Economic Cooperation (APEC) forum currency exchange rates. The results reveal that APEC currencies have time-varying effects (tend to cluster in appreciation and depreciation patterns in both the short and long term) and have generated higher total return spillover during COVID-19 (in the time domain) than the Russia-Ukraine war and SVB collapse. During COVID-19 (87.18 %) (total return spillover), impacts were more severe than the Russia-Ukraine crisis (79.49 %) and the Silicon Valley Bank collapse (75.55 %). Moreover, the South Korean won, Thai Bhat and Australian Dollar are identified as consistent shock transmitters, and Malaysian Ringgit, Philippine peso, Indonesian Rupiah, and Chinese Yuan as consistent shock receivers in the time domain. The findings have substantial repercussions for financial regulators and investors.

Study on the spatiotemporal distribution patterns and influencing factors of cultural heritage: a case study of Fujian Province

The spatiotemporal distribution characteristics of cultural heritage reveal the trajectory of human activity changes, and a deep analysis of its natural and cultural factors holds significant reference value for the overall conservation and management of cultural heritages. This study focuses on the cultural heritage at the provincial level and above in Fujian, utilizing GIS spatial analysis to explore the spatiotemporal evolution of cultural heritages and their natural and human influencing factors. The research findings are as follows: (1) The distribution of cultural heritage in Fujian exhibits a clustering pattern, with dense areas transitioning from the upstream regions of the prehistoric and pre-Qin periods to the eastern coastal areas gradually. (2) The Ming and Qing dynasties have the highest number of cultural heritages, with the type of heritage transitioning from ancient sites in the early periods to ancient architecture, and in modern times, mainly important historical sites and representative architectural heritages. (3) The overall centroid coordinates of cultural heritage reveal a shift from the northern part of Fujian to the eastern and southern parts. (4) Natural factors significantly influence the distribution of cultural heritage, with a higher concentration in plain and hilly areas, on slight slopes with gradients between 0.5° and 2.0°, and on the southern and southeastern slopes, especially within a 1-kilometer radius of rivers. (5) The creation of cultural heritage during historical periods is closely linked to the regional history, culture, political, and economic environments. The positive development of these socio-cultural factors has a promotional effect on the quantity of cultural heritage. This study demonstrates the utility and applicability of GIS spatial analysis techniques in cultural heritage research, providing a methodological framework that can be adapted and applied internationally. The findings offer insightful data that can inform targeted conservation and development strategies for cultural heritage, ensuring their effective preservation and sustainable management across different regions.

Financial resilience, growth and risk sharing in the EU

We build a financial resilience index capturing the structure and stability features of financial systems and benchmark EU financial systems against their ability to enhance stable growth and international risk sharing. The index comprises financial openness, market orientation, equity deepening, maturity structure, and institutional soundness. Our results show that: (i) EU financial systems are highly heterogeneous and converge to a clustered pattern; (ii) the index is highly significant in growth regressions, suggesting that financial structure and institutional soundness are key to enduring growth; (iii) the heterogeneity of EU financial systems has implications for the vulnerability to domestic output shocks: the risk-sharing mechanism is more effective in the market-based and institutionally sound economies that group in the top financial clusters, whereas unsmoothed consumption is higher in economies belonging to the low-resilience clusters, especially in the aftermath of the global financial crisis, when the credit channel is significantly downsized.

Transcriptome Analysis and Identification of Genes Associated with Cotton Seed Size.

Cotton seeds, as the main by-product of cotton, are not only an important raw material for edible oil and feed but also a source of biofuel. The quality of cotton seeds directly affects cotton planting and is closely related to the yield and fiber quality. However, the molecular mechanism governing cotton seed size remains largely unexplored. This study investigates the regulatory mechanisms of cotton seed size by focusing on two cotton genotypes, N10 and N12, which exhibit notable phenotypic variations across multiple environments. Developing seeds were sampled at various stages (5, 20, 30, and 35 DPA) and subjected to RNA-seq. Temporal pattern clustering and WGCNA on differentially expressed genes identified 413 candidate genes, including these related to sugar metabolism that were significantly enriched in transcriptional regulation. A genetic transformation experiment indicated that the overexpression of the GhUXS5 gene encoding UDP-glucuronate decarboxylase 5 significantly increased seed size, suggesting an important role of GhUXS5 in regulating cotton seed size. This discovery provides crucial insights into the molecular mechanisms controlling cotton seed size, helping to unravel the complex regulatory network and offering new strategies and targets for cotton breeding to enhance the economic value of cotton seeds and overall cotton yield.

Rewireable Building Blocks for Enzyme-Powered DNA Computing Networks.

Neural networks enable the processing of large, complex data sets with applications in disease diagnosis, cell profiling, and drug discovery. Beyond electronic computers, neural networks have been implemented using programmable biomolecules such as DNA; this confers unique advantages, such as greater portability, electricity-free operation, and direct analysis of patterns of biomolecules in solution. Analogous to bottlenecks in electronic computers, the computing power of DNA-based neural networks is limited by the ability to add more computing units, i.e., neurons. This limitation exists because current architectures require many nucleic acids to model a single neuron. Each additional neuron compounds existing problems such as long assembly times, high background signal, and cross-talk between components. Here, we test three strategies to solve this limitation and improve the scalability of DNA-based neural networks: (i) enzymatic synthesis for high-purity neurons, (ii) spatial patterning of neuron clusters based on their network position, and (iii) encoding neuron connectivity on a universal single-stranded DNA backbone. We show that neurons implemented via these strategies activate quickly, with a high signal-to-background ratio and process-weighted inputs. We rewired our modular neurons to demonstrate basic neural network motifs such as cascading, fan-in, and fan-out circuits. Finally, we designed a prototype two-layer microfluidic device to automate the operation of our circuits. We envision that our proposed design will help scale DNA-based neural networks due to its modularity, simplicity of synthesis, and compatibility with various neural network architectures. This will enable portable computing power for applications in portable diagnostics, compact data storage, and autonomous decision making for lab-on-a-chips.

Uncovering the spatial characteristics of global net anthropogenic nitrogen input at high resolution and across 1.42 million lake basins

Global Net Anthropogenic Nitrogen Input (NANI) at high resolution is crucial for assessing the impact of human activities on aquatic environments. Insufficient global high-resolution data sources and methods have hindered the effective examination of the global characteristics and driving forces of NANI. This study presents a general framework for calculating global NANI, providing estimates at a 5-arc-minute resolution and over 1.42 million lake basins in 2015. The results highlight the region near the Tropic of Cancer as a concentration area for high NANI and an inflection point for latitude-based accumulation variation. It also emphasizes the uneven distribution of NANI among continents, with Asia and Africa having the highest proportions, yet their high and low values are notably lower than those of Europe and South America. A similar pattern is observed in global lakes, where Asia has the smallest quantity and volume, but the highest NANI intensity. In contrast, North America and Europe have larger quantities and volumes but the lowest NANI intensity. The global distribution characteristics reveal a clustering pattern in high and low values, with 1.25 % of the area having a sum of NANI exceeding 20 %. The uncertainty analysis regarding model parameters indicates that continents with the highest NANI do not always exhibit the highest uncertainty. These results bridge the gap between global nitrogen sustainable management and anthropogenic nitrogen input. They support research on spatiotemporal changes and controlling factors of global river nutrient loads, as well as the impact of climatic factors on basin nitrogen loss and its variability.