Cluster Analysis Research Articles

Morpho-physiological traits and stress indices reveal divergence in early-stage salt stress response in elite soybean germplasm

This study assessed the salt stress response of seventy soybean genotypes by exposing seedlings to sodium chloride (NaCl) concentrations of 0, 75, 100, and 125 mM. Salinity stress significantly reduced root length, shoot length, fresh and dry root/shoot weight, and root/shoot ratio. A total of 62 genotypes germinated at 125 mM, showing significant phenotypic variation in traits such as root length, shoot length, fresh shoot weight, and dry shoot weight. Pearson’s correlation analysis revealed a strong positive correlation between most morpho-physiological traits, suggesting their interdependence. Eight salinity tolerance indices i.e., Fresh Weight Stress Tolerance Index (FWSI), Dry Weight Stress Tolerance Index (DWSI), Root Length Salinity Index (RLSI), Shoot Length Salinity Index (SLSI), Salinity Tolerance Index (STI), Salinity Susceptibility Index (SSI), Tolerance Index (TI), and Percent Reduction (PR) were calculated to assess genotypes response. Na+ concentration and antioxidant activities significantly increased under salt stress compared to the control. The activities of antioxidant enzymes, as well as the levels of reactive oxygen species (ROS) such as H2O2 and O2−, were markedly higher under salt stress compared to the control. Hierarchical cluster analysis grouped genotypes into six clusters, with clusters V and VI comprising genotypes exhibiting higher salt tolerance based on high FWSI, DWSI, and STI values, and low PR, SSI, and TI values. Eight salinity tolerance indices, including Fresh Weight Stress Tolerance Index (FWSI), Dry Weight Stress Tolerance Index (DWSI), Root Length Salinity Index (RLSI), and Salinity Tolerance Index (STI), were used to evaluate the genotypes’ responses. The study revealed that the average FWSI value for all genotypes under treatments was 54.20 ± 12.93. The highest FWSI was recorded for the genotype Black (92.52), followed by NIBGE-224 (84.70) and NIBGE-183 (81.05). In contrast, Malakand-96 had the lowest FWSI (28.49), followed by SPS-10 (32.74) and SPS-08 (34.55). PGRA-91 had the highest STI (0.83), followed by SPS-9 (0.75) and NIBGE-115 (0.72), while NIBGE-335, SPS-24, and Malakand-96 had the lowest STI values. Positive correlations were observed between root length and shoot length (0.69**), shoot length and fresh shoot weight (0.63**), and other related traits. Overall, this study identified promising soybean genotypes with varying degrees of salt tolerance. These findings can be utilized in breeding programs to develop salt-tolerant soybean varieties for salinity-affected agricultural lands.

Psychographic segmentation of alcohol quitters in behaviour change maintenance mode: utilisation of the activity interest opinion (AIO) framework

Purpose This study aims to cluster psychographic activities, interests and opinions of alcohol quitters in behaviour change maintenance mode to form segmentation bases for social policy and alcohol policy formulation that sustain the behaviour change. Design/methodology/approach A two-step cluster analysis was used to segment a cross-sectional sample of n = 501 former alcohol consumers based on their activity interest and opinion (AIO) framework through a multiple-response survey design. Findings Five clusters were found across five AIO themes of “people socialised with”, “relationship interest”, “ingredients of a good relationship”, “party activities” and “outing dressing”. The clusters were honesty-based relationship seekers, spiritual- and intellectual-based relationship seekers, attention seekers in a healthy relationship, self-conscious seeking a healthy relationship, and sincere, spiritual and intellectual-based relationship seekers. Practical implications The findings present an opportunity for social policy design that encourages alcohol-free social space creation, strong family ties, community and spiritual growth and incentives for long-term relationships as means of sustaining alcohol quitters’ behaviour. Equally, alcohol policies that match the themes of the clusters can be designed to aid sobriety sustenance. Originality/value This study advances knowledge of behaviour change maintenance and provides lifestyle segments of alcohol quitters as bases for further social and alcohol policy design to sustain gains in behaviour change.

Analysis of the Current State and Prospects for the Development of the Tourism Services Market in China

Aim. The purpose of the study is to analyze the current state of the Chinese tourism services market and the prospects for its development in the context of the public policy of the PRC.Tasks. Research objectives: perform a cluster analysis of entrepreneurial activity in the field of tourism, construct a diagram of the distribution of travel agencies by clusters; comply with the current state of the tourism services market in terms of key metrics; study government support measures for the industry; determine the prospects for the development of the tourism services market of the PRC and Russian-Chinese cooperation in the context of the implementation of the new “double circulation” strategy.Methods. Methodological basis and research methods: systematic approach, logical analysis, synthesis, cluster and regression analysis, content analysis of open sources.Results. The results obtained: the supply-side model of China’s modern tourism landscape includes three clusters; The specificity of China’s domestic tourism market is the holiday tourism segment; a new driver of economic growth is domestic tourism, the preferences of domestic tourists reflect a wide range of interests; the system of state support for tourism includes direct and indirect methods that adapt to changes in the external and internal environment.Conclusions. With a high probability, the prospects for cooperation between the PRC and the Russian Federation are assessed quite highly; favorable factors in the development of Russian-Chinese cooperation are the opening of new “windows of opportunity” after the COVID-19 pandemic, as well as the restriction of outbound tourist flows to unfriendly countries and changes in the vector towards the BRICS countries, SCO, and EAEU.

Clustering Universities According to Performance Indicators and Determination of Organizational Culture Types in Clusters

This study aims to reveal the organizational culture types of state universities in Kazakhstan, to define the dominant organizational culture type and to interpret the organizational culture types in terms of performance indicators. In order to achieve this goal, statistical data collected from 29 state universities were classified using hierarchical cluster analysis and Ward’s Technique. In this context, firstly, the concept of organizational culture and its effects on organizational performance were explained in the light of literature information, then cluster analysis was conducted on universities in terms of institutional size and performance. In addition, since a data-oriented approach was preferred in this study, universities were classified based on objective data instead of predetermined criteria. Finally, SPSS 22.0 program and Organizational Culture Assessment Tool were used to analyze the validity of the obtained data and the relationships between the variables, and as a result, the organizational culture types of universities were revealed, the dominant organizational culture type among them was determined and suggestions were given based on empirical findings in the study. It is expected that the results of this study will bring different perspectives on the effect of organizational culture on performance indicators in state universities in Kazakhstan.

축제 방문 동기에 따른 축제방문객 시장세분화 연구: 설악문화제 중심으로

The purpose of this study was to find out what the motivation factors for visiting the festival are for visitors who participated in the Gangwon State Seorak cultural festival and to divide festival visitors based on the derived motivation factors for visiting the festival. Factor analysis, cluster analysis, and ANOVA analysis were used as research analysis. As a result of performing factor analysis on 14 festival visit motivation items, four factors (novelty, escape, family affinity, and sociability) were derived. Through market segmentation analysis, visitors who attended the festival were classified into three groups. Cluster 1 was a group of 88 people (25.7%) out of the total survey respondents of 349, and was a group of general festival visitors who showed a little curiosity about the festival. It called as ‘common festival visitor’. Cluster 2 was 131 people (38.3%) and had strong ties with families. It called as ‘family festival visitor’. Cluster 3 was an enthusiastic festival visitor 123 people (36.0%) that actively accepts all parts of tourism motivation. It called as ‘enthusiastic festival visitor’. In result, cluster 2, and Cluster 3 were derived as major target to developing marketing strategies and several recommendation were suggested.

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk of metastasis. Consequently, the timely identification of biomarkers associated with lymph node metastasis is essential for improving the clinical management of NSCLC patients. In this research, the WGCNA algorithm was utilized to pinpoint genes linked to lymph node metastasis in NSCLC. A cluster analysis was carried out to investigate how these genes correlate with the prognosis and the outcomes of immunotherapy for NSCLC patients. Following this, diagnostic and prognostic models were created and validated through various machine learning methodologies. The random forest technique highlighted the importance of ARHGAP11A, leading to an in-depth examination of its role in NSCLC. By analyzing 78 tissue chip samples from NSCLC patients, the study confirmed the association between ARHGAP11A expression, patient prognosis, and lymph node metastasis. Finally, the influence of ARHGAP11A on NSCLC cells was assessed through cell function experiments. This research utilized the WGCNA technique to identify 25 genes that are related to lymph node metastasis, clarifying their connections with tumor invasion, growth, and the activation of stemness pathways. Cluster analysis revealed significant associations between these genes and lymph node metastasis in NSCLC, especially concerning immunotherapy and targeted treatments. A diagnostic system that combines various machine learning approaches demonstrated strong efficacy in forecasting both the diagnosis and prognosis of NSCLC. Importantly, ARHGAP11A was identified as a key prognostic gene associated with lymph node metastasis in NSCLC. Molecular docking analyses suggested that ARHGAP11A has a strong affinity for targeted therapies within NSCLC. Additionally, immunohistochemical assessments confirmed that higher levels of ARHGAP11A expression correlate with unfavorable outcomes for NSCLC patients. Experiments on cells showed that reducing ARHGAP11A expression can hinder the proliferation, metastasis, and stemness traits of NSCLC cells. This investigation reveals the novel insight that ARHGAP11A may function as a potential biomarker connected to lymph node metastasis in NSCLC. Moreover, reducing the expression of ARHGAP11A has demonstrated the ability to diminish tumor stemness characteristics, presenting a promising opportunity for improving treatment strategies for this condition.

Assessing population structure and morpho-molecular characterization of sunflower (Helianthus annuus L.) for elite germplasm identification.

Sunflower (Helianthus annuus L.), known for its adaptability and high yield potential, is vital in global edible oil production. Estimating genetic diversity is a key pre-breeding activity in crop breeding. The current study comprised of 48 genotypes which were assessed for their biometrical traits at department of Oilseeds, Tamil Nadu Agricultural University, during the rainy season of 2022. The lines were subsequently characterised using 103 simple sequence repeat (SSR) markers for molecular diversity analysis. The results indicated that the net nucleotide distances indicated varying genetic divergence, with subpopulations II and V showing the highest (0.056) and I and IV the lowest (0.014). Subpopulation IV exhibited the highest heterozygosity (0.352), while subpopulation III had the lowest heterozygosity and a low Fst (0.173). Principal components analysis (PCA) and hierarchical cluster analysis were employed for assessing the morphological diversity, facilitating genotype grouping and parent selection for breeding programs. The first four components cumulatively accounted for 86.72% of the total variation. Cluster Analysis grouped 48 sunflower genotypes into three clusters based on genetic diversity. COSF 13B stands out for its high head diameter, oil content, seed yield, and oil yield based on mean performance of morphological data. Principal coordinate analysis (PCoA) mirrored the groupings from the Neighbor Joining method, with the first three components explaining 27.24% of the total variation. Molecular data analysis identified five distinct clusters among the germplasm. By integrating morphological and molecular marker data with genetic distance analysis, substantial diversity was revealed with the genotypes RHA 273 and GMU 325 consistently demonstrated high oil yield per plant. The genotypes GMU 477, GMU 450, COSF 13B, RHA 102, CMS 1103B, and RHA GPR 58 have been identified as suitable parents for enhancing oil content in sunflower breeding programs. These findings also aid in selecting SSR markers for genotype characterization and in choosing diverse parents for breeding programs.

울산지역 일부 고등학생의 치과공포에 대한 관련요인 분석

This study explored the effects of personal characteristics on dental fear in some high school students in Ulsan, and studied the correlation and causal relationship between general fear and dental fear. Data collection was conducted with 401 students enrolled in three high schools from April 22 to May 30, 2024. The validity and reliability of the questionnaire tool were analyzed by applying exploratory factor analysis and principal component analysis, and individual substrate types were classified through cluster analysis. Through t-test, one-way ANOVA, and Scheffe's post-test, the difference in fear related to dental treatment and general fear according to personal characteristics was compared, and the causal relationship between personal characteristics, general fear factors, and fear factors related to dental treatment was identified through path analysis. As a result of the study analysis, female students felt more fear in general than male students, and there was a significant difference between dental fear and general fear according to individual temperament, and as a result of studying the influence relationship between each factor through path analysis, the factor that has the greatest influence on the fear related to dental treatment was found to be the fear of physical threats. On the other hand, fear varies depending on temperament in all fear factors, so a customized approach is necessary to control dental fear.

Asymptotic Results for the Estimation of the Quadratic Score of a Clustering

In cluster analysis one often finds several partitions of a data set using different clustering methods and algorithms set with a variety of hyperparameters and tunings. The number of clusters K is one of the most relevant of such hyperparameters. Cluster selection is the task of choosing the desired partitions. The Bootstrap Quadratic Scoring is a recently introduced method where the cluster selection is performed by optimizing a score attached to a partition that is based on the quadratic discriminant function. Previously, we proposed the estimation of this cluster score via bootstrap resampling and investigated the proposed estimator based on numerical experiments and real data applications. However, that earlier work did not provide theoretical guarantees. In this paper, we fill that gap. We study the asymptotic behavior of the scoring method and show that the proposed estimator converges to well-defined population counterparts.

Genetic Diversity and Population Structure Analysis of Pinus elliottii Germplasm Resources in Jiangxi Province

This study aimed to compare and assess the genetic diversity and trends among the introduced family provenance, first-cycle superior trees breeding provenance, and improved-generation superior trees breeding provenance of Pinus elliottii using EST-SSR markers. The goal was to provide a foundation for advanced genetic improvement and sustainable utilization of P. elliottii in Jiangxi Province. A total of 417 individuals were analyzed for their genetic diversity and population structure using 19 pairs of SSR markers. The analysis identified 103 alleles across all the samples, with an average of 5.421 alleles per locus. Compared to other coniferous species, P. elliottii exhibited a moderate to high level of genetic diversity (I = 0.862, He = 0.457). Analysis of the molecular variance (AMOVA) revealed that 97.90% of the genetic variation occurred within provenances, consistent with a low genetic differentiation coefficient (Fst = 0.016 < 0.05) and high gene flow (Nm = 15.715) among provenances. In addition, analysis using STRUCTURE v. 2.3.4 software divided the 417 germplasm samples into two distinct groups, corroborating the results of the principal coordinates analysis (PCoA) and the unweighted pair group method with arithmetic (UPGMA) clustering analysis. Overall, the germplasm resources of P. elliottii exhibited rich genetic diversity, with the majority of the genetic variation occurring within provenances. For the genetic improvement of high-resin-yielding slash pines, breeding programs should prioritize populations with high genetic diversity while carefully selecting superior individuals from within those populations. These findings provide a solid foundation for breeding high-resin-yielding varieties and for future research on the sustainable utilization of these valuable resources.

Radiological hazard assessment of the soil in Daquq district, Kirkuk, Iraq

Abstract This investigation assessed the baseline levels of radioactivity in the soil of Daquq district, Iraq, using the GR-460 radiation detection system. 238U, 232Th, and 40K’s specific activities were measured and the hazard indices’ values determined. The average values of the absorbed dose, annual effective dose equivalent, annual gonadal equivalent dose, and lifetime cancer risk radiological risk factors were estimated. It is important to note that some of these results exceeded the global average levels. Various multivariate statistical techniques were employed to explore the relationships between the radioactive variables and identify potential correlations amongst them. Cluster analysis was utilized to identify clusters or groups of variables that shared similar characteristics. The study aimed to gain insights into the relationships between the radioactive variables and identify any correlations between them. These analyses provided valuable information regarding the associations and patterns within the data, thus contributing to an improved understanding of the radioactive variables and their potential implications.

Insights into the historical trajectory and research trends of immune checkpoint blockade in colorectal cancer: visualization and bibliometric analysis

BackgroundColorectal cancer (CRC) is a malignant tumor that poses a significant threat to human health due to rising incidence and mortality rates. In recent years, immune checkpoint blockade (ICB) therapy, represented by Programmed death receptor 1 (PD-1), T-lymphocyte-associated protein 4 (CTLA-4), and others, has been widely applied in CRC and has achieved encouraging results in some patients and has become a hot topic in both clinical and basic research.ObjectiveThis study undertakes a comprehensive bibliometric analysis of ICB research in CRC, aiming to evaluate the current status, identify future trends, and provide scientific insights for researchers and decision-makers.MethodsUtilizing the Web of Science Core Collection (WoSCC), articles focusing on ICB in CRC from 2000 to 2022 were retrieved. Knowledge mapping and bibliometric analysis were conducted using tools such as CiteSpace, VOSviewer, SCImago Graphicay, and the R package bibliometrix.Results6,718 publications were analyzed from 24,846 institutions across 639 regions. Temporally, ICB research in CRC is rapidly advancing, led by the USA and China with extensive global collaborations. Sun Yat-sen University from China stands out as the institution with the highest number of publications. Professor Thierry Andre from Sorbonne University in France is identified as a prolific author in this field, engaging in extensive collaboration for clinical trials on a global scale. Publications related to this research topic were published in 1,142 academic journals, demonstrating a positive co-citation relationship. Key clustering and burst terms analysis indicate that current research on ICB in CRC has shifted from basic experiments to clinical trials and from universal healthcare to precision medicine.ConclusionICB therapies have shown substantial progress in CRC, highlighting their therapeutic potential. Research trends emphasize deeper drug mechanisms, treatment efficacy prediction, managing immune-related adverse events, and exploring novel drug delivery methods. Collaboration across borders remains crucial for further advancements.

Assessment of sleep patterns in dementia and general population cohorts using passive in-home monitoring technologies

BackgroundNocturnal disturbances are a common symptom experienced by People Living with Dementia (PLWD), and these often present prior to diagnosis. Whilst sleep anomalies have been frequently reported, most studies have been conducted in lab environments, which are expensive, invasive and not natural sleeping environments. In this study, we investigate the use of in-home nocturnal monitoring technologies, which enable passive data collection, at low cost, in real-world environments, and without requiring a change in routine.MethodsClustering analysis of passively collected sleep data in the natural sleep environment can help identify distinct sub-groups based on sleep patterns. The analysis uses sleep activity data from; (1) the Minder study, collecting in-home data from PLWD and (2) a general population dataset (combined n = 100, >9500 person-nights).ResultsUnsupervised clustering and profiling analysis identifies three distinct clusters. One cluster is predominantly PLWD relative to the two other groups (72% ± 3.22, p = 6.4 × 10−7, p = 1.2 × 10−2) and has the highest mean age (77.96 ± 0.93, p = 6.8 × 10−4 and p = 6.4 × 10−7). This cluster is defined by increases in light and wake after sleep onset (p = 1.5 × 10−22, p = 1.4 × 10−7 and p = 1.7 × 10−22, p = 1.4 × 10−23) and decreases in rapid eye movement (p = 5.5 × 10−12, p = 5.9 × 10−7) and non-rapid eye movement sleep duration (p = 1.7 × 10−4, p = 3.8 × 10−11), in comparison to the general population.ConclusionsIn line with current clinical knowledge, these results suggest detectable dementia sleep phenotypes, highlighting the potential for using passive digital technologies in PLWD, and for detecting architectural sleep changes more generally. This study indicates the feasibility of leveraging passive in-home technologies for disease monitoring.

Characterization of Phytoplankton Composition in Lake Maggiore: Integrated Chemotaxonomy for Enhanced Cyanobacteria Detection

Cyanobacterial blooms in lakes have increased in frequency and intensity over the past two decades, negatively affecting ecological and biogeochemical processes. This study focuses on the phytoplankton composition of Lake Maggiore, with a special emphasis on cyanobacteria detection through pigment composition. While microscopy is the standard method for phytoplankton identification, pigment-based methods provide broader spatiotemporal coverage. Between May and September 2023, five measurement campaigns were conducted in Lake Maggiore, collecting bio-geochemical and bio-optical data at 27 stations. The total Chlorophyll-a (TChl a) was measured, with concentrations ranging from 1.13 to 6.9 mg/m3. Phytoplankton pigment composition was analyzed using High-Performance Liquid Chromatography (HPLC) and the CHEMTAX approach was applied for phytoplankton classification. The results were cross-validated using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and microscopic counts. Cyanobacteria were identified based on unique pigment markers, such as carotenoids. The HPLC-derived pigment classification results aligned well with both PCA and HCA and microscopic counts verified the accuracy of the pigment-based chemotaxonomy. The study demonstrates that pigment-based classification methods, when combined with statistical analyses, offer a reliable alternative for identifying cyanobacteria and other phytoplankton groups, with potential applications in support of remote sensing algorithm development.

Latent Profiles of Passion and Categories of University Students' Passionate Activities: Difficulties in the Empirical Systematisation of Passion

ABSTRACTBoth classical and contemporary definitions of education emphasise the development of learners and the discovery of their potential. The period of systemic education at all stages is an opportunity to seek this potential both at school and outside it. These are the circumstances of the development of abilities, interests and passions. In the educational process and in the modern world, the most valued are people who are creative, entrepreneurial, open to new experiences and have a passion—since it provides energy, gives wings, is a source of a sense of freedom and meaning and helps maintain well‐being. This study aimed to explore passion profiles, considering their dimensions and categories, and the respondents' field of study and gender, in a sample of Polish university students (n = 2720). The online Self‐Report Passion Inventory (SRPI) was used. Cluster analysis identified three latent profiles, differentiated by the perceived benefits of passion, its origin and its balance with other life activities. In addition, we identified categories of passionate activities typical of each profile. Field of study and gender were represented in similar ways in the distinguished profiles, indicating the universal nature of passion. The qualitative analysis of the results revealed potential reasons for the difficulties respondents experienced while categorising passionate activities.

Analysis of 2019 Ohio Disease Intervention Specialist (DIS) Records for Syphilis Cases Using Clustering Algorithms.

Developments in natural language processing (NLP) and unsupervised machine learning methodologies (e.g., clustering) have given researchers new tools to analyze both structured and unstructured health data. We applied these methods to 2019 Ohio disease intervention specialist (DIS) syphilis records, to determine whether these methods can uncover novel patterns of co-occurrence of individual characteristics, risk factors, and clinical characteristics of syphilis that are not yet reported in the literature. The 2019 DIS syphilis records (n=1,996) contain both structured data (categorical and numerical variables) and unstructured notes. In the structured data, we examined case demographics, syphilis risk factors, and clinical characteristics of syphilis. For the unstructured text, we applied TF-IDF (term frequency multiplied by inverse document frequency) weights, a common way to convert text into numerical representations. We performed agglomerative clustering with cosine similarity using the CLUTO software. The cluster analysis yielded six clusters of syphilis cases based on patterns in the structured and unstructured data. The average internal similarities were much higher than the average external similarities, indicating that the clusters were well-formed. The factors underlying three of the clusters related to patterns of missing data. The factors underlying the other three clusters were sexual behaviors and partnerships. Notably, one of the three consisted of individuals who reported oral sex with male or anonymous partners while intoxicated, and one was comprised mainly of males who have sex with females. Our analysis resulted in clusters that were well-formed mathematically, but did not reveal novel epidemiological information about syphilis risk factors or transmission that were not already known.

Quantitative Analysis within Language Studies: An Analytical Views Based on the Bibliometrics Method

Abstract Background: While qualitative research methods have provided insights into linguistic structures and processes, the recent decades have witnessed a growing emphasis on quantitative approaches. Methodology: This study utilizes a bibliometric analysis based on the PRISMA guidelines to explore research trends in “Quantitative Analysis within Language Studies” from 2014 to 2024 by analyzing the metadata of 53,575 journal articles retrieved from a reputable database, i.e., Dimensions. Findings: The findings show that (1) there was a steady rise in publications throughout the period; (2) the total citations increased by over 113,000, representing a nearly 100-fold growth (1,141 citations in 2014 compared to 41,203 citations in 2024); (3) according to the ANZSRC 2020 classification, the field of Language, Communication and Culture has the highest number of publications (53,575), followed by Linguistics (32,121) and Language Studies (18,272); (4) there was the top 10 journals from 2014 to 2024; (5) there was a detail into the publication productivity and citation impact of 20 leading researchers; and (6) it has been extracted 1,028 terms concerning the examined query that classified into eight clusters. Conclusion: By integrating the insights gleaned from publication trends, citation analysis, leading authors, journal distribution, map-based visualizations, and cluster analysis of terms, this study provides a comprehensive and detailed picture of research trends within the search query. Originality: While current several research in quantitative language studies tends to delve into specific subfields, offering piecemeal understandings of the overall landscape, this study takes a distinct approach. By examining 53,575 articles content and metadata, this study aims to provide a more comprehensive perspective compare to previous fragmented insights.

Discovery of PANoptosis-related signatures correlates with immune cell infiltration in psoriasis.

Psoriasis is an inflammatory skin disease that relapses frequently. Keratinocyte apoptosis dysregulation plays a crucial role in the pathological mechanisms of psoriasis. PANoptosis is a process with intermolecular interaction among pyroptosis, apoptosis, and necroptosis. The mechanism of PANoptosis in the occurrence and development of psoriasis is still unclear. Here we present a novel approach by identifying PANoptosis-related signatures (PANoptosis-sig) from skin tissue of psoriasis patients and healthy controls on transcriptional and protein levels. Five PANoptosis-sig (TYMP, S100A8, S100A9, NAMPT, LCN2) were identified. Enrichment analysis showed they were mainly enriched in response to leukocyte aggregation, leukocyte migration, chronic inflammatory response and IL-17 signaling pathway. Single cell transcriptome analysis showed TYMP and NAMPT were expressed in almost all cell populations, while LCN2, S100A8 and S100A9 were significantly highly expressed in keratinocyte. We then constructed predictive and diagnostic models with the PANoptosis-sig and evaluated their performance. Finally, unsupervised consensus clustering analysis was conducted to ascertain psoriasis molecular subtypes by the PANoptosis-sig. The psoriasis cohort was divided into two distinct subtypes. Immune landscape showed that the stromal score of cluster 1 was significantly higher than cluster 2, while the immune and estimate scores of cluster 2 were expressively higher than cluster 1. Cluster 1 exhibited high expression of Plasma cells, Tregs and Mast cells resting, while cluster 2 showed high expression of T cells, Macrophages M1, Dendritic cells activated, and Neutrophils in immune infiltration analysis. And cluster 2 was more sensitive to immune checkpoints. In conclusion, our findings revealed potential biomarkers and therapeutic targets for the prevention, diagnosis, and treatment of psoriasis, enhancing our understanding of the molecular mechanisms underlying PANoptosis.

Are all luxury guests the same? A benefit segmentation of 5-star hotel customers

PurposeThis research aims at identifying segments of luxury guests based on the benefits they seek when staying at 5-star hotels. The study also wants to investigate whether ethics and sustainability have a role in differentiating the segments.Design/methodology/approachAn online panel survey allowed the collection of 508 responses with participants from Switzerland that spent at least one night in a 5-star hotel between 2022 and 2023. The survey asked about perceived benefits, sustainable tourist behavior, preferred hotel green practices, trip-related factors and socio-demographics. Hierarchical cluster analysis was employed to group participants on the benefits identified via exploratory factor analysis. The other variables were used for segment profiling.FindingsResults show the existence of three customer segments for 5-star hotels: the ethical enthusiast, the unconcerned aesthete and the practical connoisseur. These differ in the importance they assign to ethical, social and symbolic benefits, as well as functional service benefits. The relevance of sustainability in the luxury hotel experience is stronger for the ethical enthusiast.Practical implicationsLuxury hotel managers should highlight sustainable practices on social media to attract ethical enthusiasts. In addition, marketing content customization should be used to appeal to the benefits and profile of each identified segment.Originality/valueThis is the first study that provides a benefit segmentation of luxury hotel guests, and it is also the first research that considers the role of sustainability and ethics in differentiating these customers.

Research on the Identification of Rock Mass Structural Planes and Extraction of Dominant Orientations Based on 3D Point Cloud

The different spatial distribution forms of rock mass structural planes create weak zones in the rock mass, which is also a key factor in controlling rock mass stability. Accurately and efficiently identifying rock mass structural planes and obtaining their dominant orientations is critical for rock mass engineering design and construction. Traditional surveying methods for high and steep rock mass structural planes pose high safety risks, offer limited data, and make comprehensive statistical analysis difficult. This paper utilizes complex rock mass surface 3D point cloud data obtained through 3D laser scanning technology and uses the Hough space transform method to calculate the normal vectors of the 3D point cloud. Based on the difference in normal vectors and surface variation, region growing segmentation is applied to identify and extract rock mass structural planes. Additionally, the fast search and density peak clustering method (CFSFDP) is used for clustering analysis of the rock mass structural planes to obtain dominant orientations. This method was applied to a highway’s high and steep rock slope, successfully identifying 281 structural planes and two sets of dominant structural planes. The orientation of the dominant structural planes identified through RocScience Dips 7.0 analysis showed a deviation of no more than ±3°, complying with engineering standards. The research results offer a feasible solution for the identification of high and steep rock mass structural planes and the extraction of the orientation of dominant structural planes.