Cluster Integrals Research Articles

Fabricating Open‐Framework Using Polyoxovanadate Clusters for Efficient Removal of Radioactive Cs+ and Sr2+

AbstractInnovation of 137Cs+ and 90Sr2+ ion exchangers is achieved through heteroatom‐bridged integration of polyoxovanadate clusters, whose soft Lewis basic nature and large size promote the selective capture of cesium/strontium radionuclides. The radiolytically stable Na6P4V10O34·19H2O (VPO‐1) features an interlinkage of {NaV10O26} clusters and phosphorus bridges, forming nanoscale voids with hydration‐lubricated sodium ions. This structure endows VPO‐1 with remarkable exchange kinetics (k2Cs = 3.774 g mg−1 min−1; k2Sr = 1.376 g mg−1 min−1), equilibrium removal (R > 99.3% within 5–10 min), and maximum capacities (qmCs = 300.32 mg g−1; qmSr = 113.00 mg g−1). The exchange mechanism is illuminated through single‐crystal XRD by revealing cluster‐vacancy pathways and ionic‐radius‐dependent adsorption sites. VPO‐1 exhibits stability across pH = 3–12, with exceptional KdCs and KdSr values above 105 mL g−1, and high selectivity for Cs+ and Sr2+ over various competing ions. These properties enable excellent column filtration performance (R = 99%–100%) for mixed Cs+ and Sr2+ during a 3000 mL test. Deep cleaning of these ions is also possible via suction filtration using an ultra‐thin VPO‐1/PTFE membrane, with high exchange activity (R = 98.8%–99.9%) retained after simple regeneration with 2 m NaCl, offering significant potential for nuclear wastewater treatment applications.

Integrative deep immune profiling of the elderly reveals systems-level signatures of aging, sex, smoking, and clinical traits.

Aging increases disease susceptibility and reduces vaccine responsiveness, highlighting the need to better understand the aging immune system and its clinical associations. Studying the human immune system, however, remains challenging due to its complexity and significant inter-individual variability. We conducted an immune profiling study of 550 elderly participants (≥60 years) and 100 young controls (20-40 years) from the RESIST Senior Individuals (SI) cohort. Extensive demographic, clinical, and laboratory data were collected. Multi-color spectral flow cytometry and 48-plex plasma cytokine assays were used for deep immune phenotyping. Data were analyzed using unsupervised clustering and multi-dataset integration approaches. We studied 97 innate and adaptive immune cell populations, revealing intricate age- and sex-related changes in the elderly immune system. Our large sample size allowed detection of even subtle changes in cytokines and immune cell clusters. Integrative analysis combining clinical, laboratory, and immunological data revealed systems-level aging signatures, including shifts in specific immune cell subpopulations and cytokine concentrations (e.g., HGF and CCL27). Additionally, we identified unique immune signatures associated with smoking, obesity, and diseases such as osteoporosis, heart failure, and gout. This study provides one of the most comprehensive immune profiles of elderly individuals, uncovering high-resolution immune changes associated with aging. Our findings highlight clinically relevant immune signatures that enhance our understanding of aging-related diseases and could guide future research into new treatments, offering translational insights into human health and aging. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2155-project number 390874280.

Clinicopathologic Characteristics of 40 Cases of Juvenile Conjunctival Nevus.

Limited studies have been conducted on juvenile conjunctival nevus (JCN) in Asian populations. This study aims to investigate the clinical and pathologic characteristics of JCN cases among the Han ethnicity in northwest China, providing insights for pathologists and ophthalmologists in diagnosing this condition. A subset of conjunctival nevi in children and adolescents, characterized by a confluent growth pattern and lack of maturation, was identified and defined as JCN. A retrospective analysis of the clinicopathologic features of these cases was performed. The study included 40 patients aged 3-20 years, with a median age of 15.5 years. Six cases involved congenital conjunctival pigmentation. Twenty-four were male patients and 16 were female patients, with a male-to-female ratio of 1.5:1. Microscopically, nevus cells grew confluently within or beneath the epidermis and exhibited moderate cellular atypia. In some cases, the junction of the epithelium and the lamina propria layer was obscured by the integration of nevus cell clusters, mimicking epithelial invasion and potentially leading to a misdiagnosis. Over an average follow-up of 78.5 months, no recurrence or postoperative complications were observed. Diagnosing JCN is challenging because of its histologic resemblance to conjunctival melanoma. A systematic analysis of JCN can improve the recognition of its benign nature by pathologists and ophthalmologists, helping to prevent overdiagnosis of conjunctival malignant melanoma in this young population.

Dynamical evolution of massless particles in star clusters with NBODY6++GPU-MASSLESS

Context. Low-mass bodies, such as comets, asteroids, planetesimals, and free-floating planets, are continuously injected into the intra-cluster environment after expulsion from their host planetary systems. These objects can be modelled as massless particles (MLPs). Notably, the dynamics of large populations of MLPs have received little attention in the literature. Aims. We investigate the dynamical evolution of MLP populations in star clusters and characterise their kinematics and ejection rates. Methods. We present NBODY6++GPU-MASSLESS, a modified version of the N-body simulation code NBODY6++GPU that allows for fast integration of star clusters that contain large numbers of MLPs. NBODY6++GPU-MASSLESS contains routines specifically directed at the dynamical evolution of low-mass bodies, such as planets. Results. Unlike stars, MLPs do not participate in the mass segregation process. Instead, MLPs mostly follow the gravitational potential of the star cluster, which gradually decreases over time due to stellar ejections and stellar evolution. The dynamical evolution of MLPs is primarily affected by the evolution of the core of the star cluster. This is most apparent in the outer regions for clusters with higher initial densities. High escape rates of MLPs are observed before the core collapse, after which escape rates remain stable. Denser star clusters undergo a more intense core collapse, but this does not impact the dynamical evolution of MLPs. We find the speeds of escaping stars are similar to those of escaping MLPs when disregarding the high-velocity ejections of neutron stars during the first 50 Myr.

Влияние кластеров на продолжительность жизненных циклов предпринимательских экосистем в регионах России

In the context of the global economy and growing competition within the country and on the international market, the development of entrepreneurial ecosystems is an important factor for sustainable economic growth. The author distinguishes between the concepts of “entrepreneurial ecosystem” and “cluster”. In a new view of entrepreneurial ecosystems as dynamic structures that go through the stages of birth, formation, development, maturity, stabilization, renewal or decline, according to the author’s methodology, special attention is paid to the mechanism of prolongation of growth stages due to the influence of clusters. The integration of life cycle theory and clusters provides a new perspective on entrepreneurial ecosystems through the lens of clusters. The novelty lies in the role of clusters as an effective approach that promotes interaction between business, government and science to accelerate innovation processes, create sustainable cooperation networks, attract investment and reduce barriers to entry of new entrants to the market. To substantiate the decision to select the largest entrepreneurial ecosystems, a comparative analysis was carried out for the Novosibirsk Region in the context of the regions of the Siberian Federal District, for the Republic of Tatarstan – in the context of the regions of the Volga Federal District. An analysis of the economic sectors of the studied regions revealed that entrepreneurial companies not only operate in the main ones, but also form related industries. It is revealed that the industry association leads to the creation of new industries of the sixth technological order and the production of competitive goods and services, which creates an additional need for investments for the development of innovation activities by entrepreneurial ecosystem companies. The use of the OTSW analysis tool (according to V. L. Quint’s method) is substantiated. This article will help determine the approach when making a management decision to increase the vital activity of the entrepreneurial ecosystem. The formation of clusters will improve the interaction between ecosystem participants, develop entrepreneurship and stimulate innovation in the country’s regions.

Enhancing data curation with spectral clustering and Shannon entropy: An unsupervised approach within the data washing machine

In the realm of digital data proliferation, effective data curation is pivotal for extracting meaningful insights. This study explores the integration of spectral clustering and Shannon Entropy within the Data Washing Machine (DWM), a sophisticated tool designed for unsupervised data curation. Spectral clustering, known for its ability to handle complex and non-linearly separable data, is investigated as an alternative clustering method to enhance the DWM’s capabilities. Shannon Entropy is employed as a metric to evaluate and refine the quality of clusters, providing a measure of information content and homogeneity. The research involves rigorous testing of the DWM prototype on diverse datasets, assessing the performance of spectral clustering in conjunction with Shannon Entropy. Results indicate that spectral clustering, when combined with entropy-based evaluation, significantly improves clustering outcomes, particularly in datasets exhibiting varied density and complexity. This study highlights the synergistic role of spectral clustering and Shannon Entropy in advancing unsupervised data curation, offering a more nuanced approach to handling diverse data landscapes.

Study on the Economic Effects of Opening Up Guangxi Higher Education to the ASEAN

With the promotion of the “Belt and Road” Initiative, education cooperation between China and ASEAN countries has increased, highlighting the opening of education to the outside world. In this context, exploring the opening-up pattern of higher education to ASEAN countries and the regional differences it shows, as well as the positive catalytic effect of international student mobility on regional economic development, is particularly urgent and important. By using CiteSpace, we find that the economic effect of opening up higher education to the outside world has become the focus of academic attention. Closely following this hotspot, this paper analyses the current situation of Guangxi's higher education in opening up to ASEAN countries and finds that although Guangxi has achieved initial results in attracting international students from ASEAN countries, there is still much room for improvement in terms of improving the quality of education and teaching and enhancing economic attractiveness. Moreover, opening up to ASEAN countries not only directly promotes the growth of Guangxi's outward foreign direct investment but also greatly promotes mutual benefits and creates a win‒win situation between the two sides in many fields, such as education and cultural exchanges and economic and trade exchanges. Given this, the government should promote trade and investment facilitation, encourage RMB settlement, promote the integration of industry and education clusters, and create an innovation highland for higher education openness and cooperation toward ASEAN countries.

Enhancing differentiation and functionality of insulin-producing cells derived from iPSCs using esterified collagen hydrogel for cell therapy in diabetes mellitus

BackgroundIslet transplantation is a recommended treatment for type 1 diabetes but is limited by donor organ shortage. This study introduces an innovative approach for improving the differentiation and functionality of insulin-producing cells (IPCs) from iPSCs using 3D spheroid formation and hydrogel matrix as an alternative pancreatic islet source. The extracellular matrix (ECM) is crucial for pancreatic islet functionality, but finding the ideal matrix for β-cell differentiation has been challenging. We aimed to advance IPC differentiation and maturation through an esterified collagen hydrogel, comparing its effectiveness with conventional basement membrane extract (BME) hydrogels.MethodsiPSCs were differentiated into IPCs using a small molecule-based sequential protocol, followed by spheroid formation in concave microwells. Rheological analysis, scanning electron microscopy, and proteomic profiling were used to characterize the chemical and physical properties of each matrix. IPCs, both in single-cell form and as spheroids, were embedded in either ionized collagen or BME hydrogels, which was followed by assessments of morphological changes, pancreatic islet-related gene expression, insulin secretion, and pathway activation using comprehensive analytical techniques.ResultsEsterified collagen hydrogels markedly improved the structural integrity, insulin expression, and cell-cell interactions in IPC spheroids, forming densely packed insulin-expressing clusters, in contrast to the dispersed cells observed in BME cultures. Collagen hydrogel significantly enhanced the mRNA expression of crucial endocrine markers and maturation factors, with IPC spheroids showing accelerated differentiation from day 5, suggesting a faster differentiation compared to single cells in hydrogel encapsulation. Insulin secretion in response to glucose in collagen environments, with a GSIS index of 2.46 ± 0.05, exceeded those in 2D and BME, demonstrating superior pancreatic islet functionality. Pathway analysis highlighted enhanced insulin secretion capabilities, evidenced by the upregulation of genes like Secretogranin III and Chromogranin A in collagen cultures. In vivo transplantation results showed that collagen hydrogel enhanced cluster integrity, tissue integration, and insulin secretion compared to non-embedded IPCs and BME groups.ConclusionEsterified collagen hydrogels demonstrated superior efficacy over 2D and BME in promoting IPC differentiation and maturation, possibly through upregulation of the expression of key secretion pathway genes. Our findings suggest that using collagen hydrogels presents a promising approach to enhance insulin secretion efficiency in differentiating pancreatic β-cells, advancing cell therapy in diabetes cell therapy.

Boosting the Performance and Durability of Fe-N-C Fuel Cell Catalysts via Integrating Mo2C Clusters.

Carbon-supported nitrogen-coordinated iron single-atom (Fe-N-C) catalysts have been regarded among the most promising platinum-group-metal-free catalysts for oxygen reduction reaction (ORR) in proton exchange membrane fuel cells (PEMFCs). Nevertheless, their limited intrinsic activity and unsatisfactory stability have hindered their practical applications. Here, it is reported that the integration of Mo2C clusters effectively enhances the ORR activity and stability of Fe-N-C catalysts. The composite catalyst of Fe single atoms and Mo2C clusters co-embedded on nitrogen-doped carbon (FeSA/Mo2C-NC) exhibits an excellent ORR activity with a half-wave potential of 0.82V in acidic media and a high peak power density of 0.5W cm-2 in an H2-air PEMFC. Moreover, improved stability is achieved with nearly no decay under H2-air conditions for 80h at 0.4V. Experiments with theoretical calculations elucidate that the etching effect of the phosphomolybdic acid precursor optimizes the pore size distribution of the composite catalyst, thereby exposing more active sites. The Mo2C clusters modulate the electronic configuration of the Fe-N4 sites, optimizing adsorption energy for ORR intermediates and strengthening the Fe-N bond to mitigate demetalation. This work provides valuable insights into the construction of single-atom/nanoaggregate hybrid catalysts for efficient energy-related applications.

A clustered-LPSEIRS malware propagation model in complex networks

In this paper, we present a new malware propagation model that integrates epidemic spread, clustering, and link prediction techniques, tailored for complex network networks. Our model is based on the clustered-link prediction-susceptible-exposed-infected-recovered (clustered-LPSEIRS) epidemic model, which simulates malware dissemination within the network. Our findings reveal a significant decrease in the rate of malware spread compared to the traditional SEIR model, with this enhancement in containment attributed to the integration of clustering and link prediction methods. We also compute the basic reproduction ratio (R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_{0}$$\\end{document}) for our model, providing insights into the potential ramifications of malware within the network. By examining parameter variations, we enhance our understanding of the model's behavior under diverse scenarios. Additionally, we assess the influence of clustering and link prediction on mitigating malware spread, emphasizing its effectiveness in diminishing the overall impact.

On the Integration of Standard Deviation and Clustering to Promote Scalable and Precise Wi-Fi Round-Trip Time Positioning

Recently, the use of fingerprinting has been proposed for positioning using the Wi-Fi RTT estimations gathered by IEEE 802.11mc devices. Wi-Fi RTT poses a challenge on scalability due to the location-specific traffic injected in the network, which may limit the data traffic transmissions of other Wi-Fi users. In this respect, fingerprinting has been regarded as a promising scalable technique, compared to multilateration. While coupling other metrics should bring relief to the system, reducing the number of APs to which RTT measurements are requested alleviates the burden in specific cells. But how far may we go? This paper assesses several methods aimed at reducing the Wi-Fi RTT overhead while preserving the precision of the calculated position. The use of the Wi-Fi RTT standard deviation is assessed for the first time, being especially useful when the number of RTT procedures is minimized. The application of clustering can also improve position estimates while leveraging bandwidth for other users’ purposes.

Nonlinear Ensemble Deep Learning Model for Energy Consumption Prediction with Bayesian Optimization

Accurate prediction of electric energy consumption is crucial for efficient load dispatching, energy utilization, and grid operation. Traditional statistical and classical machine learning methods struggle with the nonlinear nature of energy consumption data, often leading to higher prediction errors. Additionally, deep learning models using a single approach face challenges such as convergence to local minima and poor generalization. This paper proposes a nonlinear ensemble deep learning model for residential energy consumption prediction, incorporating Bayesian optimization for hyperparameter tuning. The model combines Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and 1D Convolutional Neural Networks (1D-CNN), leveraging their powerful nonlinear feature learning capabilities. A k-means clustering approach is used to preprocess and reduce variability in the data, enhancing the ensemble model's performance. The ensemble model was tested on real energy consumption data from two districts in Addis Ababa, showing significant improvements in prediction accuracy with lower MAE, RMSE, and MAPE values compared to single models and un-clustered data. The integration of clustering and Bayesian optimization further enhanced model generalizability and minimized overfitting, demonstrating the effectiveness of a nonlinear approach in capturing complex energy consumption patterns.

A Bibliometric Exploration of Multiple Criteria Decision Aid and Clustering—A Conceptual Taxonomy

ABSTRACTThis work explores the intersection of Multiple Criteria Decision Aid (MCDA) and clustering techniques, revealing unexploited potential and novel perspectives arising from their integration, challenging their conventional separation. It serves as a compass, guiding researchers through a bibliometric exploration and a conceptual taxonomy consolidating existing knowledge. Employing a two‐fold methodology, we first sketch the field's contours through a bibliometric lens, uncovering its intellectual structure, thematic landscape, and social dynamics. Then, using science mapping techniques like co‐word analysis, historiography, and collaboration network analysis, we examine patterns, revealing an interconnected mosaic of concepts. Our findings unveil a natural grouping into three categories: (1) Mixed‐yet‐not‐integrated approaches, explores sequential applications—clustering followed by MCDA or vice versa—where one method precedes and informs the other. (2) ‘Relational/ordered clustering’ leveraging criteria dependency to refine structures. (3) Using MCDA to improve clustering mechanics through similarity metrics, domain knowledge incorporation, and robustness. We conclusively propose a taxonomy along three axes: Units of Analysis, Instrumentalisation, and Objective. The key takeaway emphasises the collaborative potential of MCDA, envisioning a landscape where the integration of MCDA and clustering not only enhances existing methodologies but also spawns innovative paradigms, fostering a symbiotic relationship that transcends conventional boundaries.

OH‐Induced Surface Reconstitution in Single Atoms and Clusters Integrated Electrocatalysts for Self‐Adaptive Oxygen Electrocatalysis

AbstractThe integration of atom clusters and single atoms into a unified system represents a desirable approach for attaining enhanced catalytic performance. Nonetheless, the controllable synthesis of a single‐atom and nanocluster integrated system (SA‐NC) faces considerable challenges, and the mechanisms underlying the catalytic activity remain poorly understood. In this research, a cobalt‐based catalyst containing both coordinatively unsaturated single‐atom (CoN3) and small nanoclusters (Co@SA‐NC) is synthesized. The Co@SA‐NC not only facilitates charge and mass transfer due to the interconnected long‐range micromorphology, thus endowing efficient oxygen electrocatalytic reaction (ORR/OER), but also undergoes surface reconfiguration upon OH adsorption at high potentials in alkaline ORR/OER conditions. More appealingly, this OH‐involved reconfigured adaptive structure promotes optimization of energy barriers owing to the dynamic regulation from the bridged OH between Co single‐atom and cluster in the whole reaction process. Specific to the application metrics, a zinc–air battery assembled using the Co@SA‐NC catalyst exhibit targeted power density enhancement with 270 mW cm−2 in an alkaline medium. This work offers an effective insight into the study of SA‐NC catalytic reaction pathways for efficient ORR/OER catalysis.

Single-Cell RNA-Seq Debiased Clustering via Batch Effect Disentanglement.

A variety of single-cell RNA-seq (scRNA-seq) clustering methods has achieved great success in discovering cellular phenotypes. However, it remains challenging when the data confounds with batch effects brought by different experimental conditions or technologies. Namely, the data partitions would be biased toward these nonbiological factors. Meanwhile, the batch differences are not always much smaller than true biological variations, hindering the cooperation of batch integration and clustering methods. To overcome this challenge, we propose single-cell RNA-seq debiased clustering (SCDC), an end-to-end clustering method that is debiased toward batch effects by disentangling the biological and nonbiological information from scRNA-seq data during data partitioning. In six analyses, SCDC qualitatively and quantitatively outperforms both the state-of-the-art clustering and batch integration methods in handling scRNA-seq data with batch effects. Furthermore, SCDC clusters data with a linearly increasing running time with respect to cell numbers and a fixed graphics processing unit (GPU) memory consumption, making it scalable to large datasets. The code will be released on Github.

TMB Signature-Related RCAN2 Promotes Apoptosis by Upregulating EHF/DR5 Pathway in Hepatocellular Carcinoma.

The tumour mutation burden (TMB) is a valuable indicator of the accumulation of somatic mutations, and is thought to be associated with the biological behaviour and prognosis of tumours. However, the related genetic mechanism for these association is still unclear. The aim of the present study was to identify the key gene(s) associated with TMB in hepatocellular carcinoma (HCC) and to investigate its biological functions, downstream transcription factors, and mechanism of action. Patients in The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) database were classified according to TMB signature-related genes. Key genes related to the TMB signature and tumour prognosis were identified. Immunohistochemistry and Quantitative Real-Time Polymerase Chain Reaction (qPCR) were then used to assess gene expression in clinical HCC tissues and HCC cells. Cells with altered gene expression were evaluated for the effect on cell proliferation and apoptosis, both in vitro and in vivo. Three independent databases and cell sequencing data were used to identify the mechanisms involved and the downstream transcription factors. The mechanism was also studied by altering the expression of downstream transcription factors in vitro. The integrated cluster (IC) 2 group, characterized by 99 TMB signature-related genes, showed a significant different TMB score compared to the IC1 group (p < 0.001), as well as more favourable tumour prognosis (p = 0.031). We identified five key prognostic genes that were differentially expressed between IC2 and IC1 and were associated with overall survival. The expression of one of these key prognostic genes, RCAN2, was negatively correlated with TMB in 18 out of 33 tumour types examined. A high level of RCAN2 was correlated with better overall survival in HCC (p = 0.0009). Overexpression of RCAN2 enhanced apoptosis in vitro and in vivo, whereas knockdown of RCAN2 attenuated apoptosis. The mechanism by which RCAN2 promotes apoptosis may involve upregulation of the expression of ETS homologous factor (EHF) and of death receptor 5 (DR5). Downregulation of RCAN2 expression was found to correlate with elevated TMB in multiple cancer types. RCAN2 was also found to be a biomarker of HCC prognosis, and to promote the apoptosis of HCC cells through the EHF/DR5 pathway. These findings provide a new perspective on systemic treatment for advanced HCC with a high TMB.

An efficient cre-based workflow for genomic integration and expression of large biosynthetic pathways in Eubacterium limosum.

Acetogenic Clostridia are obligate anaerobes that have emerged as promising microbes for the renewable production of biochemicals owing to their ability to efficiently metabolize sustainable single-carbon feedstocks. Additionally, Clostridia are increasingly recognized for their biosynthetic potential, with recent discoveries of diverse secondary metabolites ranging from antibiotics to pigments to modulators of the human gut microbiota. Lack of efficient methods for genomic integration and expression of large heterologous DNA constructs remains a major challenge in studying biosynthesis in Clostridia and using them for metabolic engineering applications. To overcome this problem, we harnessed chassis-independent recombinase-assisted genome engineering (CRAGE) to develop a workflow for facile integration of large gene clusters (>10 kb) into the human gut acetogen Eubacterium limosum. We then integrated a non-ribosomal peptide synthetase gene cluster from the gut anaerobe Clostridium leptum, which previously produced no detectable product in traditional heterologous hosts. Chromosomal expression in E. limosum without further optimization led to production of phevalin at 2.4 mg/L. These results further expand the molecular toolkit for a highly tractable member of the Clostridia, paving the way for sophisticated pathway engineering efforts, and highlighting the potential of E. limosum as a Clostridial chassis for exploration of anaerobic natural product biosynthesis.

Establishing a straightforward I-SceI-mediated recombination one-plasmid system for efficient genome editing in P. putida KT2440.

Pseudomonas putida has become an increasingly important chassis for producing valuable bioproducts. This development is not least due to the ever-improving genetic toolbox, including gene and genome editing techniques. Here, we present a novel, one-plasmid design of a critical genetic tool, the pEMG/pSW system, guaranteeing one engineering cycle to be finalized in 3 days. The pEMG/pSW system proved in the last decade to be valuable for targeted genome engineering in Pseudomonas, as it enables the deletion of large regions of the genome, the integration of heterologous gene clusters or the targeted generation of point mutations. Here, to expedite genetic engineering, two alternative plasmids were constructed: (1) The sacB gene from Bacillus subtilis was integrated into the I-SceI expressing plasmid pSW-2 as a counterselection marker to accelerated plasmid curing; (2) double-strand break introducing gene I-sceI and sacB counterselection marker were integrated into the backbone of the original pEMG vector, named pEMG-RIS. The single plasmid of pEMG-RIS allows rapid genome editing despite the low transcriptional activity of a single copy of the I-SceI encoding gene. Here, the usability of the pEMG-RIS is shown in P. putida KT2440 by integrating an expression cassette including an msfGFP gene in 3 days. In addition, a large fragment of 12.1 kb was also integrated. In summary, we present an updated pEMG/pSW genome editing system that allows efficient and rapid genome editing in P. putida. All plasmids designed in this study will be available via the Addgene platform.

Studies on the quantification and time-sequence development of integration of industry and education level in higher vocational education-based on the panel data of China’s provincial from 2016 to 2022

An extensive assessment index system was developed to evaluate the integration of industry and education in higher vocational education. The system was designed using panel data collected from 31 provinces in China between 2016 and 2022. The study utilized the entropy approach and coupled coordination degree model to examine the temporal and spatial changes in the level of growth of the integration of industry and education in higher vocational education, as well as the factors that impact it. In order to examine how the integration of industry and education in higher vocational education develops over time and space, as well as the factors that affect it, we utilized spatial phasic analysis, Tobit regression model, and Dagum’s Gini coefficient. The study’s findings suggest that between 2016 and 2022, the integration of industry and education in higher vocational education showed a consistent improvement in overall development. Nevertheless, there are still significant regional differences, with certain areas showing limited levels of integration, while the bulk of regions are either in a state of low integration with high clustering or low integration with low clustering. Most locations showed either a “low-high” or “low-low” level of agglomeration, indicating a significant degree of spatial concentration, with a clear trend of higher concentration in the east and lower concentration in the west. The progress of industrial structure and the degree of regional economic development have a substantial impact on the amount of integration of industry and education in higher vocational education. There is a notable increase in the amount of integration between industry and education in higher vocational education, which has a favorable effect. Conversely, the local employment rate has a substantial negative effect on this integration. Moreover, the direct influence of industrial structure optimization is restricted. The Gini coefficient of the development level of integration of industry and education in higher vocational education exhibits a slight rising trend. Simultaneously, there is a varying increase in the Gini coefficient inside the group and a decrease in the Gini coefficient between the groups. The disparities in the level of integration between Industry and Education in the provincial area primarily stem from inter-group variations across the locations. To promote the integration of industry and education in higher vocational education, it is recommended to strengthen policy support and resource allocation, address regional disparities, improve professional configuration, and increase investment in scientific and technological innovation and talent development.

The Integration of Dual Evaluation and Minimum Spanning Tree Clustering to Support Decision-Making in Territorial Spatial Planning

Spatial planning, a policy instrument for creating sustainable environments that meet the needs of the current and future generations, has been implemented extensively worldwide. However, it is difficult for urban planners to thoroughly determine the spatial value of a territory and make informed decisions regarding the efficient utilization of regional resources in the real world. This study proposes a novel methodological framework for spatial pattern optimization that can guide future land use by integrating Minimum Spanning Tree (MST) clustering with a comprehensive evaluation system (dual evaluation). Furthermore, the validity of this framework is demonstrated through a case study of territorial spatial planning in Deyang, China. The findings indicate that (1) the methodological framework presented in this study offers valuable guidance for the spatial arrangement of territorial resources, especially in practical projects; and (2) the combination of dual evaluation and MST clustering can facilitate automatic regionalization to identify spatial clusters exhibiting functional similarity in terms of land use. By focusing on methodological advancements, this study concludes that the integration of dual evaluation (DE) and MST clustering not only simplifies the identification of optimal land-use patterns but also promotes a more systematic and efficient approach to support spatial planning.