Cluster Analysis Of Data Research Articles

Make the Right Call: formative evaluation for a water safety programme for adults in Western Australia

Background: Preventing drowning in adults is a complex, multifaceted injury prevention issue. The adult drowning rate in Australia is continuing to rise. In 2023, more than half of those who drowned were aged over 45 years. While there has been a call for robust, evidence-informed drowning prevention interventions across the life course, evidence of evaluated, drowning prevention programmes for adults is lacking. For more than a decade, drowning prevention researchers and practitioners in Western Australia (WA) have worked in partnership to design and evaluate evidence-informed programmes. This paper describes formative research to develop ‘Make the Right Call’ (MTRC), an adult water safety programme in WA. Methods: A staged, mixed-methods, formative evaluation was undertaken to inform the design of MTRC. Stages: The approach involved cluster analysis of coronial data 2008–2018 ( n = 93), interviews with adults aged 45–64 years ( n = 10) and 65 years and older ( n = 15), theory mapping, survey design, content and face validity testing of a baseline questionnaire with content experts ( n = 11) and a refined instrument to collect knowledge, and data on norms and water-based activity. The subsequent MTRC programme comprised a media campaign, community swimming and safety classes, lifejacket trade-in, subsidised first-aid training and community education. Discussion: The results of each stage informed the design and evaluation of a new evidence-informed and theory-driven drowning prevention programme for adults aged 45 years and older, delivered by the peak drowning prevention agency in WA, Royal Life Saving Western Australia. An investment in mixed-methods research by the partnership added rigour and credibility to the programme and evaluation design. Conclusion: To the best of our knowledge, this is the first adult water safety programme to apply a theory-driven, evidence-informed approach to its development, implementation and evaluation. The partnership was vital in maximising and accelerating the acceptable transfer of results to enhance knowledge creation and, ultimately, the design of the MTRC drowning prevention programme.

Remnant cholesterol, clinical characteristics and mortality in 41.767 patients from LIPIDOGRAM 2004-2015 studies - the factor analysis for mixed data cluster analysis

Abstract Introduction Remnant cholesterol (remnant-C) contributes to residual cardiovascular risk and is produced by metabolism of triglyceride rich proteins. Aim To assess association between clinical characteristics of patients with elevated remnant-C in a large cohort of patients under the care of primary care physicians. Methods The LIPIDOGRAM studies were carried out in the primary care in Poland in 2004, 2006 and 2015. Patients (n=47,398) recruited in all 16 administrative regions in Poland and physicians were proportionally distributed to the number of inhabitants in each administrative region. Each patient was asked to fill the questionnaire on risk factors, chronic diseases, treatment and lifestyle. In the present analysis we included patients with body mass index (BMI )>18.5 kg/m2 aged 18-75 years. We set the cut-off point of fasting remnant cholesterol at >30 mg/dL (>0.77 mmol/L) that differentiated subjects at high risk of cardiovascular events. We carried out Factor Analysis for Mixed Data (FAMD) cluster analysis to discern groups of patients with similar clinical profiles. Results 41,767 patients, for which we had all relevant data, were included in the analysis. Follow-up rate was 97.8%. Three FAMD-derived clinical characteristics patterns accounted for 47.8% of the total variance and were retained for further analysis. The first pattern was associated with higher prevalence of metabolic syndrome (MetS), higher waist circumference, higher levels of non-HDL-C and remnant-C levels. The second pattern was distinguished by lipid parameters, particularly higher HDL-C levels, and was not significantly associated with comorbid conditions. The third pattern was linked to male sex, previous myocardial infarction, smoking, age, lower remnant-C levels, and reduced prevalence of obesity. All patterns were associated with 5-year mortality. The hazard ratio (HR) for mortality per 1 standard deviation (SD) increase in the first pattern score was 1.18 (95% CI: 1.16-1.22, p<0.001). Patients with clinical characteristics corresponding to second pattern had more favorable outcome HR (per 1SD score increase) – 0.75, 95%CI (0.73-0.78, p<0.001). Patients in third cluster, similarly to patients in cluster 1 had increased mortality HR (per 1SD score) – 1.10, (95%CI:1.06-1.15, p <0.001). Higher (>0.77 mmol/l / 30 mg/dl) remnant-C cholesterol significantly contributed to classification of patients into first and third clusters. It was positively associated with first pattern (r=0.64, p<0.001), but negatively with the third pattern (r=0.39, p<0.001). Conclusions Elevated Remnant-C was associated with clinical pattern typical for metabolic syndrome. Interestingly, lower remnant-C levels in patients with comorbidities and more advanced age may not automatically be indicative of a better prognosis – this requires further investigation.Figure 1.Figure 2.

Single-cell transcriptomics reveal diverging pathobiology and opportunities for precision targeting in scleroderma-associated versus idiopathic pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) involves progressive cellular and molecular change within the pulmonary vasculature, leading to increased vascular resistance. Current therapies targeting nitric oxide (NO), endothelin, and prostacyclin pathways yield variable treatment responses. Patients with systemic sclerosis-associated PAH (SSc-PAH) often experience worse outcomes than those with idiopathic PAH (IPAH). Lung tissue samples from four SSc-PAH, four IPAH, and four failed donor specimens were obtained from the Pulmonary Hypertension Breakthrough Initiative (PHBI) lung tissue bank. Single-cell RNA sequencing (scRNAseq) was performed using the 10X Genomics Chromium Flex platform. Data normalization, clustering, and differential expression analysis were conducted using Seurat. Additional analyses included gene set enrichment analysis (GSEA), transcription factor activity analysis, and ligand-receptor signaling. Pharmacotranscriptomic screening was performed using the Connectivity Map. SSc-PAH samples showed a higher proportion of fibroblasts and dendritic cells/macrophages compared to IPAH and donor samples. GSEA revealed enriched pathways related to epithelial-to-mesenchymal transition (EMT), apoptosis, and vascular remodeling in SSc-PAH samples. There was pronounced differential gene expression across diverse pulmonary vascular cell types and in various epithelial cell types in both IPAH and SSc-PAH, with epithelial to endothelial cell signaling observed. Macrophage to endothelial cell signaling was particularly pronounced in SSc-PAH. Pharmacotranscriptomic screening identified TIE2, GSK-3, and PKC inhibitors, among other compounds, as potential drug candidates for reversing SSc-PAH gene expression signatures. Overlapping and distinct gene expression patterns exist in SSc-PAH versus IPAH, with significant molecular differences suggesting unique pathogenic mechanisms in SSc-PAH. These findings highlight the potential for precision-targeted therapies to improve SSc-PAH patient outcomes. Future studies should validate these targets clinically and explore their therapeutic efficacy.

Joint regression analysis of clustered current status data with latent variables.

Clustered current status data frequently occur in many fields of survival studies. Some potential factors related to the hazards of interest cannot be directly observed but are characterized through multiple correlated observable surrogates. In this article, we propose a joint modeling method for regression analysis of clustered current status data with latent variables and potentially informative cluster sizes. The proposed models consist of a factor analysis model to characterize latent variables through their multiple surrogates and an additive hazards frailty model to investigate covariate effects on the failure time and incorporate intra-cluster correlations. We develop an estimation procedure that combines the expectation-maximization algorithm and the weighted estimating equations. The consistency and asymptotic normality of the proposed estimators are established. The finite-sample performance of the proposed method is assessed via a series of simulation studies. This procedure is applied to analyze clustered current status data from the National Toxicology Program on a tumorigenicity study given by the United States Department of Health and Human Services.

Ultrasensitive SERS Detection of Five β-Blockers Achieved Using Chemometrics with a Two-Dimensional Substrate Formed by Large-Sized Ag@SiO2 Nanoparticles.

We report on a surface-enhanced Raman scattering (SERS) platform for the detection of five beta-blockers (β-blockers): atenolol, esmolol, labetalol, sotalol, and propranolol. Key to this platform was a two-dimensional substrate formed by self-assembling large Ag@SiO2 nanoparticles (Ag@SiO2 NPs) on a silicon wafer. The close arrangement of these large nanoparticles on the surface generated a strong and uniform electromagnetic field, which enhanced SERS signal intensity for the detection of small amounts of the target molecules. The intensities of characteristic peaks of the five β-blocker drugs increased linearly with the increase of their concentrations in the range of 10-5 to 10-8 mol/L. The detection limits were 10-10 mol/L for propranolol, 10-9 mol/L for atenolol, labetalol, and sotalol, and 10-8 mol/L for esmolol. Determination of these five β-blocker drugs added to human urine samples, using a portable Raman spectroscopy instrument, showed quantitative recovery (93-101%). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) of SERS spectral data improved the differentiation among these five β-blockers. This study highlights the potential of the developed SERS platform for rapid, on-site detection of illicit drugs and for antidoping screening.

Clustering Functional Data With Measurement Errors: A Simulation-Based Approach.

Clustering analysis of functional data, which comprises observations that evolve continuously over time or space, has gained increasing attention across various scientific disciplines. Practical applications often involve functional data that are contaminated with measurement errors arising from imprecise instruments, sampling errors, or other sources. These errors can significantly distort the inherent data structure, resulting in erroneous clustering outcomes. In this article, we propose a simulation-based approach designed to mitigate the impact of measurement errors. Our proposed method estimates the distribution of functional measurement errors through repeated measurements. Subsequently, the clustering algorithm is applied to simulated data generated from the conditional distribution of the unobserved true functional data given the observed contaminated functional data, accounting for the adjustments made to rectify measurement errors. We illustrate through simulations show that the proposed method has improved numerical performance than the naive methods that neglect such errors. Our proposed method was applied to a childhood obesity study, giving more reliable clustering results.

Subclonal Populations Detected by Flow Cytometry in Chronic Lymphocytic Leukemia

Abstract Introduction Chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) is a mature, small B-cell lymphoma that typically co-expresses CD5 and CD23. Molecular-targeted therapies have produced remarkable therapeutic benefits in CLL, yet many patients are resistant to treatment. This study aims to identify subpopulations in CLL by conventional flow cytometry and clustering analysis. Methods One-hundred-and-forty-one CLL cases were taken from a public database and analyzed by uniform manifold approximation and projection (UMAP). Differences in size, granularity, and intensities of CD19, CD20, CD79a, CD5, CD23, CD10, and FMC7 were plotted with BD FACSDiva Software. Eighteen cases with a new diagnosis of CLL at our institution between January 1, 2021 to December 31, 2022 were analyzed. Only peripheral blood samples were included. Markers including forward scatter, side scatter, and fluorescent intensities of CD19, CD20, CD5, CD23, CD10, surface kappa, and surface lambda were used to identify discrete CLL clusters in UMAP plots. Results: Public Dataset Clustering analysis of conventional flow cytometry data from 141 cases from a public database reveals discrete populations of cells exhibiting varying sizes, granularity and fluorescent intensities. Among the 141 cases, 33 (23.4%) had 1 cluster, 64 (45.4%) with 2 clusters, 42 (29.8%) with 3 clusters and 2 (1.4%) with 4 clusters. Among the cases with 2 clusters, a smaller population of cells deviated from the main neoplastic population. This smaller population had a higher forward and side scatter, and brighter intensities in CD45 and CD19, consistent with prolymphocytes. Among the cases with 3 clusters, a third population recurrently had a smaller size by forward scatter. A few cases additionally had varying intensities by CD23 and CD19. Among the 2 cases with 4 clusters, only slight deviations by UMAP were seen and may be artifactual. Institutional Dataset Clustering analysis of 18 cases at our institution revealed complex patterns of cell clusters exhibiting varying sizes, granularity and surface receptor expression intensity. Among the 18 cases, 1 (5.6%) had 1 cluster, 1 (5.6%) with 2 clusters, 10 (55.6%) with 3 clusters, 3 (16.7%) with 4 clusters, and 3 (16.7%) with 5 clusters. Similar to the public dataset, the third population, in addition to the predominant population and prolymphocytes, consistently varied by lower forward scatter. Additional clusters, such as a population with varying CD23 signal intensities, were identified in some cases. Conclusions In our study, multiple clonal subsets in CLL/SLL are detected by conventional flow cytometry analysis. These subclonal populations may result in therapy resistance for these patients, and identifying them by flow cytometry analysis may provide additional prognostic information in the clinical setting. Furthermore, the ability to detect these subclonal populations may allow for isolation of specific clones for future investigation in research settings.

Uterine mesenchymal tumours harboring the KAT6B/A::KANSL1 gene fusion represent a distinct type of uterine sarcoma based on DNA methylation profiles.

Uterine mesenchymal tumours harboring KAT6B/A::KANSL1 gene fusions typically exhibit histological and immunophenotypic overlap with endometrial stromal and smooth muscle tumours. To date it remains uncertain whether such neoplasms should be regarded as variants of smooth muscle or endometrial stromal neoplasm, or if they constitute a distinct tumour type. In this study we investigated DNA methylation patterns and copy number variations (CNVs) in a series of uterine tumours harboring KAT6B/A::KANSL1 gene fusions in comparison to other mesenchymal neoplasms of the gynecological tract. Unsupervised hierarchical clustering and t-SNE analysis of DNA methylation data (Illumina EPIC array) identified a distinct cluster for 8/13 KAT6B/A::KANSL1 tumours (herein referred to as core cluster). The other 5 tumours (herein referred to as outliers) did not assign to the core cluster but clustered near various other tumour types. CNV analysis did not identify significant alterations in the core cluster. In contrast, various alterations, including deletions at the CDKN2A/B and NF1 loci were identified in the outlier group. Analysis of the DNA methylation clusters in relation to histological features revealed that while tumours in the core KAT6B/A::KANSL1 cluster were histologically bland, outlier tumours frequently exhibited "high-grade" histologic features in the form of significant nuclear atypia, increased mitotic activity and necrosis. Three of the five patients with outlier tumours died from their disease while clinical follow-up in the remaining two patients was limited (less than 12months). In comparison, none of the 7 out of 8 patients with tumors in the core KAT6B/A::KANSL1 sarcoma cluster, where follow-up was available, died from disease. Furthermore, only 1 out of 7 patients recurred (mean follow-up of 30months). In conclusion, KAT6B/A::KANSL1 uterine sarcoma is a molecularly unique type of uterine tumour that should be recognized as a distinct entity. These tumors typically exhibit low-grade histologic features but are occasionally morphologically high-grade; the latter have a DNA methylation profile different from the typical low-grade neoplasms and may be associated with aggressive behaviour.

Influence of the painting medium on the alteration process of smalt in oil paintings studied using combined K and Co K-edge XANES

This study investigates the alteration processes affecting the smalt pigment in historical artworks through a combination of analytical techniques including X-ray Absorption Near Edge Structure (XANES) spectroscopy at the potassium (K) and cobalt (Co) K-edges, coupled with scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) and micro X-ray fluorescence elemental mapping (µ-XRF). Laboratory mock-up samples prepared with different oil binding media were artificially aged, revealing varying rates of smalt degradation. µ-XANES analyses at the K K-edge provided grain-scale insights into smalt alteration, highlighting the influence of binding media composition on degradation kinetics. Crossed clustering data analysis over K µ-XANES acquired on both model samples and historical cross-sections corroborated findings and identified distinct spectral signatures associated with different stages of smalt degradation. Furthermore, identification of potential transformation products such as potassium alum in historical samples emphasized the dynamic nature of pigment alteration processes.

Data Mining Technology for Smart Campus in Behavior Association Analysis of College Students

The data in smart campuses is complex and massive, with insufficient utilization, and existing data processing methods have many limitations. Therefore, in order to improve the efficiency of data processing in universities and assist in student management, a data processing method integrating cluster analysis and association rule mining is proposed. The proposed method is divided into two parts. Firstly, an improved K-Means model based on information entropy and density optimization is constructed for clustering analysis of student consumption, learning, and other data; Secondly, use the improved Mapping Apriori to obtain the correlation between student grades, consumption records, and learning behavior. The clustering results on student consumption data show that the average accuracy of ED-K-Means clustering is 97.41%, which is 12.8%, 8.5% and 4.0% higher than the comparison algorithm. The result of the correlation between consumption level and achievement shows that when the amount of consumption is less than 1500 yuan, the student’s achievement is directly proportional to the amount of consumption. Therefore, the proposed method can effectively mine and analyze student behavior data, which has important practical significance for intelligent management in universities.

Morphological variation of endemic Gymnocypris chilianensis (Cyprinidae) collected from three inland water systems in Qilian Mountains, China

To explore the morphological differences among different geographic populations of Gymnocypris chilianensis, 29 measurable traits and 13 landmarks on 191 individuals of 10 geographic populations were collected from three different inland water systems, i.e., Shiyang River, Heihe River, and Shule River, in Qilian Mountains, China. Multivariate statistical methods were used to compare morphological differences among the populations, such as principal component analysis, cluster analysis, discriminant analysis, and visual analysis of landmark data. The results showed that: (1) There were significant morphological differences between groups of G. chilianensis. The Shiyang River basin differs significantly from populations in the other two basins. (2) The inter-population differences were mainly reflected in the length of the trunk and tail, and the difference between trunk and caudal fin had the most obvious characteristics, proving the tail being the main direction of variation. This research provides an effective theoretical basis for the fine management and accurate protection of G. chilianensis germplasm resources.

Integrated nasopharyngeal airway metagenome and asthma genetic risk endotyping of severe bronchiolitis in infancy and risk of childhood asthma.

Infants with bronchiolitis are at increased risk for developing asthma. Growing evidence suggests bronchiolitis is a heterogeneous condition. However, little is known about its biologically distinct subgroups based on the integrated metagenome and asthma genetic risk signature and their longitudinal relationships with asthma development. In a multi-center prospective cohort study of infants with severe bronchiolitis (i.e., bronchiolitis requiring hospitalization), we profiled nasopharyngeal airway metagenome and virus at hospitalization, and calculated the polygenic risk score of asthma. Using similarity network fusion clustering approach, we identified integrated metagenome-asthma genetic risk endotypes. We also examined their longitudinal association with the risk of developing asthma by age six years. Of 450 infants with bronchiolitis (median age, 3 months), we identified five distinct endotypes-characterized by their nasopharyngeal metagenome, virus, and asthma genetic risk profiles. Compared with endotype A infants (who clinically resembled "classic" bronchiolitis), endotype E infants (characterized by a high abundance of H. influenzae, high proportion of RV-A and RV-C infections, and high asthma genetic risk) had a significantly higher risk for developing asthma (35.9% versus 16.7%; ORadj, 2.24; 95%CI, 1.02-4.97; p=0.046). The pathway analysis showed that endotype E had enriched microbial pathways (e.g., glycolysis, L-lysine, arginine metabolism) and host pathways (e.g., IFNs, IL-6/JAK/STAT3, fatty acids, MHC, and immunoglobin-related) (FDR<0.05). Additionally, endotype E had a significantly higher proportion of neutrophils (FDR<0.05). In this multi-center prospective cohort study of infant bronchiolitis, the clustering analysis of integrated-omics data identified biologically distinct endotypes with differential risks for developing asthma.

Metabolic phenotypes and vitamin D response in the critically ill: A metabolomic cohort study

Background & AimsAlthough vitamin D deficiency is common in critically ill patients, randomized controlled trials fail to demonstrate benefits of supplementation. We aimed to identify distinct vitamin D3 responsive metabolic phenotypes prior to trial intervention of high-dose vitamin D3 by applying machine learning clustering method to metabolomics data from the Correction of Vitamin D Deficiency in Critically Ill Patients (VITdAL-ICU) trial. MethodsIn the randomized, placebo-controlled VITdAL-ICU trial, critically ill adults received placebo or high-dose vitamin D3. To distinguish vitamin D3 responsive metabolic phenotypes prior to intervention, we implemented consensus clustering with partitioning around medoids algorithm to the plasma metabolome data before randomization. Individual metabolite differences were determined utilizing linear mixed-effects regression models stratified for metabolomic phenotypes with false discovery rate adjustment. The association between vitamin D3 supplementation and 180-day mortality was evaluated in each metabolic phenotype, applying multivariable logistic regression analysis. ResultsIn 453 critically ill adults, the study identified 4 distinct metabolic phenotypes (clusters A. N=134; B. N=123; C. N=92; D. N=104). We found differential metabolic pathway patterns in the four clusters. Specifically, branched chain amino acid catabolic metabolites, long-chain acylcarnitines and diacylglycerol species are significantly increased in a specific metabolic phenotype (cluster D) following high-dose vitamin D3. Further, in cluster D high-dose vitamin D3 supplementation had a significantly lower adjusted odds of 180-day mortality after controlling age, sex, Simplified Acute Physiology Score II, admission diagnosis, and baseline 25-hydroxyvitamin D (OR 0.28 (95%CI, 0.09–0.89); P=0.03). In metabotype A, B, and C, high-dose vitamin D3 supplementation was not significantly associated with lower 180-day mortality following multivariable adjustment. ConclusionIn this post-hoc cohort study of the VITdAL-ICU trial, the clustering analysis of plasma metabolome data identified biologically distinct metabolic phenotypes. Among clusters, we found the different associations between high-dose vitamin D3 supplementation and specific metabolite pathways as well as 180-day mortality. Our findings facilitate further research to validate metabolic phenotype-targeted strategies for critical illness treatments.

Carbohydrate accumulation patterns in mangrove and halophytic plant species under seasonal variation

This study investigates the impact of carbohydrate accumulation in mangrove and halophytic plants on their response to abiotic stress. Using soil analysis and FT-IR spectroscopy, key species (Sueda nudiflora, Aeluropus lagopoides, Avicennia marina) were examined for seasonal changes in sugar content (reducing sugars, total soluble sugars, starch). The elevated carbohydrate levels may serve as an indication of the plant’s ability to adapt to different environmental conditions throughout the year. This accumulation enables plants to adapt to variations in their environment, assuring their survival and functionality during periods of environmental fluctuation. Halophytic plants’ sugar content peaked during the monsoon, suggesting biotic adaptations. The mangrove Avicennia marina had year-round sugar levels. PCA and Hierarchical Cluster Analysis revealed sugar accumulation trends across species and seasons. Partial Least Squares (PLS) analysis revealed correlations between soil characteristics and sugar content, suggesting plant-microbe interactions. K-means clustering and correlation analysis of FT-IR data revealed sugar composition and resource allocation trade-offs. These findings shed light on the role of carbohydrate metabolism in enabling coastal plants to endure stress. Gaining insight into these mechanisms can enhance sustainable agriculture in challenging environments and shed light on plant adaptations to evolving environmental conditions, especially biotic interactions.

Association of dietary habits with general and abdominal obesity in Korean children and adolescents: cluster analysis of nationwide population survey data.

Childhood obesity is a growing global health concern, but few studies have investigated dietary factors specifically related to obesity and abdominal obesity in children and adolescents. Herein, we aimed to identify the dietary factors affecting childhood obesity in Korean children and adolescents. Data from the Korea National Health and Nutrition Survey (KNHANES) VIII were analyzed using K-means clustering analysis to identify distinct clusters based on nine variables related to dietary habit, nutritional status, and nutritional education. Multiple logistic regression analysis was used to examine the association between incident obesity risk and the different clusters. We enrolled 2,290 participants aged 6-18 years, and separated them into two distinct clusters; Healthy and Unhealthy Dietary Habit Groups, clusters 1 and 2, respectively. Cluster 1 was characterized by a lower obesity prevalence, healthier dietary habits (regular breakfast consumption; fruit and vegetable, reduced total energy, and lower protein and fat intakes), and greater nutritional education than Cluster 2. After adjusting for confounders, compared with Cluster 1, Cluster 2 demonstrated a significantly higher prevalence (OR [95% CI]) of both general and abdominal obesity (1.49 [1.05-2.13], p=0.027 and 1.43 [1.09-1.88], p=0.009). Maintaining optimal dietary quality and patterns are crucial to prevent childhood obesity. Further research is warranted to explore specific dietary interventions tailored to different clusters to effectively address childhood obesity.

Childhood Trauma Questionnaire-based child maltreatment profiles to predict efficacy of the Cognitive Behavioral Analysis System of Psychotherapy versus non-specific psychotherapy in adults with early-onset chronic depression: cluster analysis of data from a randomised controlled trial

Child maltreatment is a broadly confirmed risk factor for mental and physical illness. Some psychological treatments specifically target mental health conditions associated with child maltreatment. For example, the Cognitive Behavioral Analysis System of Psychotherapy (CBASP) focuses on maladaptive interpersonal behaviours in chronic depression. However, how the assessment of child maltreatment could inform personalised treatment is unclear. We used data from a previously published clinical trial to investigate whether a pre-established child maltreatment clustering approach predicts differential outcomes after CBASP versus non-specific supportive psychotherapy in patients with early-onset chronic depression. We did a cluster analysis of data from a previous randomised controlled trial of unmedicated adult outpatients with early-onset chronic depression who were treated at eight university clinics and psychological institutes in Germany with 32 sessions of CBASP or non-specific supportive psychotherapy. Participants were eligible for the original trial if they were aged 18-65 years; had major depressive disorder (MDD) with an early onset and duration of at least 2 years, current MDD superimposed on a pre-existing dysthymic disorder, or recurrent MDD with incomplete remission between episodes as defined by DSM-IV; and had a score of at least 20 points on the 24-item Hamilton Rating Scale for Depression (HRSD-24). Participants were included in the current study if they had completed the short form of the Childhood Trauma Questionnaire (CTQ) at trial baseline. We used an agglomerative hierarchical clustering approach to derive child maltreatment clusters from individual patterns across the five domains of the CTQ. We used linear mixed models to investigate whether clustering could predict differential clinical outcomes (change in symptom severity on the HRSD-24) up to 2 years after treatment onset. People with lived experience were involved in the current study. 253 patients (129 [51%] treated with CBASP and 124 [49%] with supportive psychotherapy) had complete CTQ records and were included in the analysis. 169 (67%) participants were women, 84 (33%) were men, and the mean age was 45·9 years (SD 11·7). We identified seven child maltreatment clusters and found significant differences in treatment effects of CBASP and supportive psychotherapy between the clusters (F(6,948·76)=2·47; p=0·023); differences were maintained over the 2-year follow-up. CBASP was superior in distinct clusters of co-occurring child maltreatment: predominant emotional neglect (change in β -6·02 [95% CI -11·9 to -0·13]; Cohen's d=-0·98 [95% CI -1·94 to -0·02]; p=0·045), predominant emotional neglect and abuse (-6·39 [-10·22 to -2·56]; -1·04 [-1·67 to -0·42]; p=0·0011), and emotional neglect and emotional and physical abuse (-9·41 [-15·91 to -2·91]; -1·54 [-2·6 to -0·47]; p=0·0046). CTQ-based cluster analysis can facilitate identification of patients with early-onset chronic depression who would specifically benefit from CBASP. Child maltreatment clusters could be implemented in clinical assessments and serve to develop and personalise trauma-informed care in mental health. The German Research Foundation and the German Federal Ministry of Education and Research.

The cluster analysis of traditional Chinese medicine authenticity identification technique assisted by chemometrics

This study explore the authenticity identification technique of traditional Chinese medicine (TCM) using chemometrics in conjunction with cluster analysis. A clustering Gaussian mixture model was constructed and applied for the data clustering analysis of four types of TCM. Chemical measurements combined with discrete wavelet transform (DWT), Fourier transform infrared spectroscopy (FTIR), and Fourier self-deconvolution (FSD) were utilized for the detailed differentiation of Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium. Differences in the attenuated total reflection-FTIR (ATR-FTIR) spectra among the four TCMs were observed. Utilizing clustering algorithms, the one-dimensional DWT of the infrared spectra of samples was employed for the authentication of Chinese herbal medicines. The model demonstrates optimal performance throughout 2000 rounds of network training. The accuracy (88.6 %), sensitivity (86.5 %), and specificity (82.7 %) of the model constructed in this study significantly surpassed those of the CNN model: accuracy (67.7 %), sensitivity (70.4 %), and specificity (68.5 %) (P < 0.05). By setting the cluster size K = 5 and the number of Gaussian mixture model components to 5, the model effectively fits the actual number of categories within the dataset. Infrared spectroscopy analysis revealed distinct carbon-oxygen stretching vibration absorption peaks between 1025 and 1200 cm−1 for Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium, indicating strong absorption peaks of carbohydrates. A comprehensive structural information analysis revealed a similarity of above 0.982 among the four types of TCM. Combined with chemometrics and intelligent algorithm-based cluster analysis, successful and accurate authentication of TCM authenticity was achieved, providing an effective methodology for quality control in TCM.

웰니스 관광자원으로서 템플스테이 구성요소 분석: 웰니스 인식에 따른 템플스테이 참가자 세분화를 중심으로

Purpose: The purpose of this study is to analyze the components of Templestay from a wellness perspective, based on an understanding of the participants. By doing so, the study aims to evaluate the value of Templestay as a wellness tourism resource and derive relevant implications. Methods: A survey was conducted among Templestay participants, yielding a sample size of 201 responses. Through cluster analysis of the sample data, Templestay participants were segmented based on their level of wellness perception. Subsequently, an Importance-Performance Analysis was conducted on the Templestay components for each segmented group. By comparing the analysis results across the groups, differences in the perception of the Templestay components were identified. Results: Templestay participants were categorized into three distinct groups based on their level of wellness perception: the Wellness Enjoyment Group, characterized by a very high level of wellness perception; the Wellness Pursuit Group, which exhibited a high level of wellness perception; and the Wellness Indifference Group, which demonstrated an average level of wellness perception. Statistical analysis revealed significant differences among these groups regarding their perceptions of Templestay components. A comparison of the Importance-Performance Analysis across the groups highlighted clear distinctions. Conclusion: This study differs from prior research by confirming the value of Templestays as wellness tourism resources through participant segmentation and analysis of Templestay components. Furthermore, the findings suggest practical implications, indicating the need for investment and improvements in Templestay facilities.

Virulence, structure, and triadimefon sensitivity of the Puccinia striiformis f. sp. tritici population in Shaanxi Province, China.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is the most destructive fungal disease affecting wheat in China, especially in Shaanxi Province, an important epidemiological region connecting the western Pst over-summer regions and the central and eastern spring epidemic regions in the country. In the present study, 291 Pst isolates from Shaanxi Province were studied for their virulence using two sets of wheat differentials, population structure using single-nucleotide polymorphism (SNP) markers, and sensitivity to fungicide. When the isolates were tested on the Chinese differentials of 19 wheat cultivars, 72 races were identified, which belonged to three groups, including the Guinong 22 group (48.45%), Hybrid 46 group (31.62%), and Suwon 11 group (19.93%). The three most predominant races were CYR34 (15.46%), G22-14 (11.68%), and CYR32 (10.65%). When the isolates were tested on the 18 Yr single-gene differentials, 95 races were identified, but none of the isolates were virulent to either Yr5 or Yr15. Cluster analyses of the virulence data based on the two sets of differentials and the SNP marker data consistently separated the Shaanxi Pst population into two clusters in the central part and southern part of the Province. Triadimefon sensitivity testing across different concentrations showed a broad range of half-maximal effective concentration (EC50) values, from 0.03 to 5.99 μg mL-1, with a mean EC50 of 0.46 μg mL-1. The majority of isolates (90.72%) were sensitive to the fungicide. The correlation analyses of the virulence, SNP marker, and the triadimefon sensitivity data showed no significant correlations, except a logarithmic relationship between the EC50 value and the number of avirulence factors. This study is the first to determine the relationship of virulence and SNP markers with triadimefon sensitivity in a regional Pst population.The findings provide valuable insights for breeding resistant wheat cultivars and integrated management of stripe rust.

Conditional quasi‐likelihood inference for mean residual life regression with clustered failure time data

AbstractIn the analysis of clustered failure time data, Cox frailty models have been extensively studied by incorporating frailty with a prespecified distribution to address potential correlation of data within clusters. In this paper, we propose a frailty proportional mean residual life regression model to analyze such data. A novel conditional quasi‐likelihood inference procedure is developed, utilizing a stochastic process and the inverse probability of censoring weighting (IPCW) to form estimating equations for regression parameters. Our proposal employs conditional inference based on a penalized quasi‐likelihood to address within‐cluster correlation without need to specify the frailty distribution, bringing the method closer to what suffices for real‐world applications. By adopting the Buckley–James estimator in the IPCW, the method further allows for dependent censoring. We establish asymptotic properties of the proposed estimator and evaluate its finite sample performance via simulation studies. An application to the data from a multi‐institutional breast cancer study is presented for illustration.