Identification of Counterfeit Alcoholic Beverages Using Cluster Analysis in Principal-Component Space

Applying hierarchical grey relation clustering analysis to geographical information systems – A case study of the hospitals in Taipei City

5241652 System for performing rule partitioning in a rete networkWilliam Barabash, William Yerazunis assigned to Digital Equipment Corporation

Rheumatoid arthritis (RA) is one of the most prevalent chronic inflammatory and angiogenic diseases. The aim of this study was to evaluate the anti-inflammatory and anti-angiogenic activities in vitro of eight diterpenoids isolated from Daphne genkwa. LC-MS was used to identify diterpenes isolated from D. genkwa. The anti-inflammatory and anti-angiogenic activities of eight diterpenoids were evaluated on LPS-induced macrophage RAW264.7 cells and TNF-α-stimulated human umbilical vein endothelial cells (HUVECs) using hierarchical cluster analysis (HCA) and principal component analysis (PCA). The eight diterpenes isolated from D. genkwa were identified as yuanhuaphnin, isoyuanhuacine, 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, yuanhuagine, isoyuanhuadine, yuanhuadine, yuanhuaoate C and yuanhuacine. All the eight diterpenes significantly down-regulated the excessive secretion of TNF-α, IL-6, IL-1β and NO in LPS-induced RAW264.7 macrophages. However, only 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl markedly reduced production of VEGF, MMP-3, ICAM and VCAM in TNF-α-stimulated HUVECs. HCA obtained 4 clusters, containing 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, isoyuanhuacine, isoyuanhuadine and five other compounds. PCA showed that the ranking of diterpenes sorted by efficacy from highest to lowest was 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, yuanhuaphnin, isoyuanhuacine, yuanhuacine, yuanhuaoate C, yuanhuagine, isoyuanhuadine, yuanhuadine. In conclusion, eight diterpenes isolated from D. genkwa showed different levels of activity in LPS-induced RAW264.7 cells and TNF-α-stimulated HUVECs. The comprehensive evaluation of activity by HCA and PCA indicated that of the eight diterpenes, 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl was the best, and can be developed as a new drug for RA therapy.

BackgroundGaucher disease (GD) is a rare, autosomal recessive lysosomal storage disorder that adversely affects life expectancy and health-related quality of life (HRQOL). Although HRQOL questionnaires are available for type 1 GD, they are not suitable for patients with the neuronopathic types 2 and 3 GD who have neurological symptoms that develop during early childhood or adolescence. Here we report the development of a language-validated HRQOL questionnaire specifically for patients with neuronopathic types 2 and 3 GD in Japan, which is the first step toward HRQOL questionnaire provision for all types of GD in the future.MethodsIn February and March 2021, semi-structured interviews were conducted by the authors (supported by qualified interviewers) with patients and/or their caregivers (for patients < 16 years old) who were recruited from a Japanese patient association, the Association of Gaucher Disease Patients in Japan. Qualitative analysis of interview transcripts was used to identify major themes and key topics within those themes. Hierarchical cluster analysis and co-occurrence network analysis were performed to map relationships between commonly occurring words. The study is registered at the UMIN Clinical Trials Registry (https://www.umin.ac.jp/ctr/index.htm [UMIN000042872]).ResultsThree main themes emerged from qualitative analysis: treatment status, patient burden, and social support systems. Key topics within each theme included hearing impairment, visual impairment, difficulty swallowing, difficulty speaking, involuntary movement of extremities, epileptic seizures, and body aches (treatment status); anxiety about symptoms, difficulty with exercise and work, anxiety about continuing treatment, anxiety about going out, and tiredness from hospital visit or treatment (patient burden); and dissatisfaction about government service, lack of social support, and information exchange in the patient association (social support systems). Commonly used words and the relationships between words identified through the hierarchical cluster and co-occurrence network analyses supported these themes and topics.ConclusionsThe themes and topics identified in this analysis were specific to patients with types 2 and 3 GD and will be used to inform the development of a HRQOL questionnaire specifically for patients with all GD types.

BackgroundKnee osteoarthritis (KOA) is a pathological condition prevalent in the general population. Exercise and pharmaceutical approaches are first-line treatments and the end-stage treatment is total knee replacement (TKR). Interestingly, around...

In France, alluvial aquifers provide 45% of the freshwater used for drinking, agriculture, and industry (Maréchal and Rouillard, 2020). These aquifers are often hydraulically connected to rivers and are particularly vulnerable due to their proximity to the surface. This proximity makes them sensitive to anthropogenic pressures both quantitatively and qualitatively. The current study aims to better understand groundwater-surface water interactions in an anthropized alluvial aquifer in the lower Rhône Valley. This goal will be achieved using multivariate statistical methods on hydrogeochemical and stable water isotope data. Two datasets were analyzed. One dataset contained inert tracers (Cl−, Br−, δ2H and δ18O) and included 667 water samples (40 rainwater, 110 surface water and 517 groundwater). The other dataset contained major and minor ions (Ca2+, Mg2+, K+, Na+, Cl−, SO42-, alkalinity, NO3−, Br− and U(VI)) and stable water isotopes (δ2H and δ18O) data., and included 374 water samples (37 surface water and 337 groundwater). First, a hierarchical cluster analysis (HCA) was applied to both datasets to better understand groundwater recharge and the geochemical processes that control groundwater chemistry in the study area. We used the recently developed t-distributed stochastic neighbor embedding (t-SNE) (Van Der Maaten and Hinton, 2008) method and principal component analysis (PCA) to assist with the cluster analysis and visualization. When HCA, PCA, and t-SNE were applied to inert tracers dataset, the results first revealed the distribution of groundwater on the study site between two recharge sources: the Rhône River and rainfall. Water samples collected along the Rhône River boundary are characterized by highly depleted δ2H and δ18O signatures, indicating the significant influence (up to 80%) of the Rhône on the recharge of the alluvial aquifer in this area. In contrast, groundwater samples collected in the northern and northwestern parts of the site showed highly enriched δ2H and δ18O signatures, similar to those of rainfall indicating dominant recharge from local precipitation. Others water samples are characterized by intermediate δ2H and δ18O signatures, falling between the signatures of rainfall and the Rhône River, indicating mixed recharge. These water samples are predominantly distributed in the southern part of the site. Second, the results revealed the response of the alluvial groundwater to exceptional climatic events. In 2022, particular isotopic signatures with higher deuterium excess and high chloride concentrations were observed in rainwater and the Rhône River, which are reflected in groundwater. This suggests an influence of continental air masses originating from the Sahara Desert (Xu-Yang et al., 2025). When HCA, PCA, and t-SNE were applied to the second dataset containing hydrogeochemical and stable water isotope data, the results identified water samples with high uranium and chloride concentrations, likely due to historical pollution. This study shows that t-SNE is a promising tool for assessing groundwater-surface water interactions in an alluvial aquifer when used to assist in cluster analysis. Compared with PCA, t-SNE can better identify hidden information and perform much better with complex, nonlinear hydrogeochemical, and stable water isotope data. ReferencesMaréchal and Rouillard, 2020.https://doi.org/10.1007/978-3-030-32766-8_2Van Der Maaten and Hinton, 2008.Res.9,2579–2625.Xu-Yang, et al., 2025.https://doi.org/10.1126/sciadv.adr9192

This work demonstrated a method combining reversed-phase high-performance liquid chromatography (RP-HPLC) with chemometrics analysis to identify the authenticity of Ranae Oviductus. The fingerprint chromatograms of the Ranae Oviductus protein were established through an Agilent Zorbax 300SB-C8 column and diode array detection at 215 nm, using 0.085% TFA (v/v) in acetonitrile (A) and 0.1% TFA in ultrapure water (B) as mobile phase. The similarity was in the range of 0.779–0.980. The fingerprint chromatogram of Ranae Oviductus showed a significant difference with counterfeit products. Hierarchical clustering analysis (HCA) and principal component analysis (PCA) successfully identified Ranae Oviductus from the samples. These results indicated that the method established in this work was reliable.

Este trabajo analiza los procesos de la transición sanitaria (TS) y de la segunda transición demográfica (STD) en Argentina durante la primera década del milenio actual. A partir de la revisión de literatura especializada y utilizando bases de datos oficiales, se proponen indicadores empíricos para su evaluación y se examinan las articulaciones entre ambos procesos. En total, se seleccionaron y describieron 31 indicadores considerados proxy de estos procesos, para las 23 provincias argentinas y la Ciudad Autónoma de Buenos Aires. Posteriormente se realizó un análisis de clúster jerárquico y aglomerativo, siguiendo el método de Ward. Se identificaron cuatro clústeres de jurisdicciones argentinas, cuya distribución fue valorada espacialmente. Los clústeres presentaron diferentes grados de avance en ambas transiciones. Los hallazgos muestran que en el país existe un gradiente socioeconómico en la conformación de los clústeres, y que la sincronicidad entre la STD y la TS se expresa a escala espacial y social.

Supervised learning methods (eg. PLS-DA, SVM, etc.) have been widely used with laser-induced breakdown spectroscopy (LIBS) to classify materials; however, it may induce a low correct classification rate if a test sample type is not included in the training dataset. Unsupervised cluster analysis methods (hierarchical clustering analysis, K-means clustering analysis, and iterative self-organizing data analysis technique) are investigated in plastics classification based on the line intensities of LIBS emission in this paper. The results of hierarchical clustering analysis using four different similarity measuring methods (single linkage, complete linkage, unweighted pair-group average, and weighted pair-group average) are compared. In K-means clustering analysis, four kinds of choosing initial centers methods are applied in our case and their results are compared. The classification results of hierarchical clustering analysis, K-means clustering analysis, and ISODATA are analyzed. The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS.

Indonesia, based on natural resource potential, has great potential to achieve beef self-sufficiency. The contribution of this sector is not only limited to meeting food needs in the form of beef, but also includes economic aspects such as providing employment opportunities, industrial raw materials, and increasing the income of local farmers. This shows that the development of this sector has great potential in supporting food security and improving community welfare. Therefore, research was conducted on performance indicators that could influence the performance of the cattle farming sector in Indonesia in 2022 using cluster analysis. Cluster analysis is a statistical method that identifies groups of samples based on similar characteristics. Cluster analysis has two methods, namely hierarchical and non-hierarchical. This research focuses on classifying regions in Indonesia into groups based on similar characteristics. In this research, cluster analysis assumptions will be tested, namely the multivariate normal distribution test, conducting cluster analysis using hierarchical and non-hierarchical methods, characterizing the data in each cluster, then drawing conclusions and suggestions from the research results. Based on the research results obtained on data characteristics, it was found that variables tend to have a variety of data. Hierarchical cluster analysis uses the single linkage method which has an optimum number of clusters of 4. The highest number of cluster members is in cluster 1. Then cluster 1 shows the highest performance in the cattle farming sector. In non-hierarchical cluster analysis using the k-means method which has an optimum number of clusters of 5. The highest number of cluster members is in cluster 4. Then clusters 2, 3 and 4 show higher performance in the cattle farming sector compared to clusters 1 and 5 .

Hierarchical clustering analysis is applied to the positioning of two simultaneously-occurring intrusion events in the case of a dual Mach-Zehnder interferometer used for intrusion detection. To simulate the two intrusion events, the sensing fibers of the dual Mach-Zehnder interferometer are heavily knocked at two different positions simultaneously. Then the clockwise (CW) and counter-clockwise (CCW) signals are loaded into a personal computer through a data acquisition module, and analyzed by Fourier transform method for determination of the time delay between the two signals. Hierarchical clustering analysis is then employed twice for dividing the data points in a feature space into several clusters according to the conditions required. To locate the two intrusions, the first clustering analysis is performed on the data points formed by signals detected in a 10 ms time period, with the centroid of the largest cluster being the location of a single intrusion event. Then, 100 pairs of CW and CCW signals detected sequentially are analyzed to give 100 locations. These 100 locations and their CP values (each standing for a ratio of a given spectral amplitude to the summation of the spectral amplitudes over the frequency band of 2500 to 5000 Hz) constitute 100 data points in a feature space for the second hierarchical clustering analysis, which then determines the respective locations of the two intrusion events. In the test of a 1036 m long fiber perimeter, we demonstrated an accuracy to within 21.55 m.

Near-mine exploration via soil geochemistry multivariate analysis at the Almas gold province, Central Brazil: A study case

Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values in 52.6% of the data, which were handled by a hybrid random forest approach and bootstrap method (RF-Bs). The present approach addresses large missing values and also reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) in the search for the optimum minimal value. Cluster analysis was used to classify stations or grid points into different rainfall regimes. Hierarchical clustering analysis (HCA) of rainfall data reveal the pattern of behavior of the rainfall regime in a specific region by identifying homogeneous clusters. According to the HCA, four distinct and homogenous regions were recognized. Then, the principal component analysis (PCA) technique was used to homogenize the rainfall series and optimally reduce the long-term rainfall records into a few variables. Moreover, PCA was applied to monthly rainfall data in order to validate the results of the HCA analysis. On the basis of the 75% of cumulative variation, 14 factors for the Dry season and the Rainy season, and 12 factors for the Inter-monsoon season, were extracted among the components using varimax rotation. Consideration of different groupings into these approaches opens up new advanced early warning systems in developing recommendations on how to differentiate climate change adaptation- and mitigation-related policies in order to minimize the largest economic damage and taking necessary precautions when multiple hazard events occur.

Central nervous system (CNS) tumors are severe health conditions with increasing incidence in the last years. Different biological, environmental and clinical factors are thought to have an important role in their epidemiology, which however remains unclear. The aim of this pilot study was to identify CNS tumor patients' subtypes based on this information and to test associations with tumor malignancy. 90 patients with suspected diagnosis of CNS tumor were recruited by the Neurosurgery Unit of IRCCS Neuromed. Patients underwent anamnestic and clinical assessment, to ascertain known or suspected risk factors including lifestyle, socioeconomic, clinical and psychometric characteristics. We applied a hierarchical clustering analysis to these exposures to identify potential groups of patients with a similar risk pattern and tested whether these clusters associated with brain tumor malignancy. Out of 67 patients with a confirmed CNS tumor diagnosis, we identified 28 non-malignant and 39 malignant tumor cases. These subtypes showed significant differences in terms of gender (with men more frequently presenting a diagnosis of cancer; p = 6.0 ×10-3) and yearly household income (with non-malignant tumor patients more frequently earning ≥25k Euros/year; p = 3.4×10-3). Cluster analysis revealed the presence of two clusters of patients: one (N=41) with more professionally active, educated, wealthier and healthier patients, and the other one with mostly retired and less healthy men, with a higher frequency of smokers, personal history of cardiovascular disease and cancer familiarity, a mostly sedentary lifestyle and generally lower income, education and cognitive performance. The former cluster showed a protective association with the malignancy of the disease, with a 74 (14-93) % reduction in the prevalent risk of CNS malignant tumors, compared to the other cluster (p=0.026). These preliminary data suggest that patients' profiling through unsupervised machine learning approaches may somehow help predicting the risk of being affected by a malignant form. If confirmed by further analyses in larger independent cohorts, these findings may be useful to create potential intelligent ranking systems for treatment priority, overcoming the lack of histopathological information and molecular diagnosis of the tumor, which are typically not available until the time of surgery.

We aimed to establish latent subtypes of histopathological patterns in failed metal-on-metal hip replacements. Tissue samples of the synovia from the neocapsule were retrieved from 284 revised ASR (Articular Surface Replacement) hip replacements and analyzed histologically. Hierarchical cluster analysis and polytomous latent class analysis were performed to establish the underlying structure and relationships of the histological observations and to find similar cohorts of cases. Clustering analyses suggested four distinct subtypes that could be readily and reasonably labeled and mapped against a recent consensus statement. The first two subtypes showed synovial necrosis, lymphocyte sheets, and abundant or thin histiocyte sheets. In addition, the first subtype showed abundant germinal centers and no metal particles either extracellularly or intracellularly. Metal particles were, however, seen in the second subtype. Hence, the first subtype was labeled "immunologic Type IV neosynovitis" and the second subtype "abrasion-induced inflammatory lymphocytic Type I neosynovitis." The third and fourth subtypes showed no perivascular and diffuse lymphocytes, but a higher number of metal particles intracellularly and extracellularly. The third subtype had synovial necrosis along with granulomas and was labeled "abrasion-induced necrotic Type I neosynovitis," whereas the fourth subtype had readily intact synovial lining, and this subtype was labeled "abrasion-induced foreign body Type I neosynovitis." Histopathological findings in failed MoM hips are not just one wide entity. These hips evince four different histological patterns that also differ at the macroscopic level. Moreover, the often stated "ALVAL-type reaction" seems to be dualistic in nature, which is a novel finding.