Ward's Clustering Method Research Articles

Integrating Ward’s Clustering Stratification and Spatially Correlated Poisson Disk Sampling to Enhance the Accuracy of Forest Aboveground Carbon Stock Estimation

In forest resource surveys, using sampling methods to estimate aboveground carbon stock (ACS) can significantly reduce survey costs. This study improves the accuracy of ACS estimation by optimizing the stratified sampling design. The sampling process was divided into two stages: stratification and intra-stratum sampling. For stratification, remote sensing features were used as stratification variables, and a spatial clustering stratification method was introduced. For intra-stratum sampling, a composite method, Spatially Correlated Poisson Disk Sampling (SCPDS), was proposed. Using Random Forest (RF) and the sample points selected by SCPDS, the ACS was estimated and compared with traditional sampling methods for Pinus densata in Shangri-La, Yunnan, China. The results showed that (1) by selecting effective stratification variables (e.g., texture features), the required sample size was reduced by up to 19.35% compared to that of simple random sampling; (2) the Ward clustering method greatly improved stratification heterogeneity; (3) for intra-stratum sampling, the SCPDS method ensured spatial independence within strata, particularly at low sampling rates (1%–5%), where its error was significantly lower than that of other methods, indicating greater stability and improved accuracy; (4) the SCPDS-based model achieved the best fitting accuracy, with R2 = 0.886. The total carbon stock of Pinus densata using RF was 7,872,787.5 t, closely matching forest management inventory (FMI) data. Through sampling, even with a relatively small sample size, the representative plots can still accurately reflect ACS estimates that are consistent with those derived from large-scale plot surveys. Thus, the optimized stratified sampling method effectively reduced sampling costs while significantly enhancing the stability and accuracy of the results.

Genome-Wide Association-Based Identification of Alleles, Genes and Haplotypes Influencing Yield in Rice (Oryza sativa L.) Under Low-Phosphorus Acidic Lowland Soils.

Rice provides poor yields in acidic soils due to several nutrient deficiencies and metal toxicities. The low availability of phosphorus (P) in acidic soils offers a natural condition for screening genotypes for grain yield and phosphorus utilization efficiency (PUE). The objective of this study was to phenotype a subset of indica rice accessions from 3000 Rice Genome Project (3K-RGP) under acidic soils and find associated genes and alleles. A panel of 234 genotypes, along with checks, were grown under low-input acidic soils for two consecutive seasons, followed by a low-P-based hydroponic screening experiment. The heritability of the agro-morphological traits was high across seasons, and Ward's clustering method identified 46 genotypes that can be used as low-P-tolerant donors in acidic soil conditions. Genotypes ARC10145, RPA5929, and K1559-4, with a higher grain yield than checks, were identified. Over 29 million SNPs were retrieved from the Rice SNP-Seek database, and after quality control, they were utilized for a genome-wide association study (GWAS) with seventeen traits. Ten quantitative trait nucleotides (QTNs) for three yield traits and five QTNs for PUE were identified. A set of 34 candidate genes for yield-related traits was also identified. An association study using this indica panel for an already reported 1.84 Mbp region on chromosome 2 identified genes Os02g09840 and Os02g08420 for yield and PUE, respectively. A haplotype analysis for the candidate genes identified favorable allelic combinations. Donors carrying the superior haplotypic combinations for the identified genes could be exploited in future breeding programs.

Двоетапний метод розв’язання задачі комівояжера на основі генетичного алгоритму

One of the main tasks in logistics is to find the most efficient route in the traveling salesman problem on a given transportation network, allowing for the servicing of the maximum number of customers while considering certain criteria. In the typical traveling salesman problem, the objective function is most often the route length or duration. However, such a formulation does not account for the subjectivity in evaluating the travel durations at different stages, which may be influenced by various objective and subjective factors. The problem of the traveling salesman with fuzzily defined travel durations between network nodes is considered. The approach to solving the problem is based on a genetic algorithm with enhanced mutation procedures and population diversity generation. Trapezoidal fuzzy numbers are used to formalize fuzzy values, presented in the generalized case based on the application of Gaussian distribution and corresponding characteristics. A two-stage scheme for solving the optimization problem of the traveling salesman on a given transportation network is proposed, combining the genetic algorithm and Ward's clustering method. In the first stage, a clustering procedure is conducted using Ward's method. In the second stage, based on the obtained set of clusters and the identified optimal inter-cluster distances, route duration optimization is performed within the clusters. To test the efficiency of the developed method, two types of models were generated: one with a limited number of available paths and one with a fully connected topology. Computational experiments were conducted to apply the developed method to solving the traveling salesman problem with travel durations between cities represented as fuzzy numbers. A comparison of numerical calculation results was performed. The obtained results demonstrated the effectiveness of preliminary clustering when using the genetic algorithm to find the best local optima under the same input parameters.

Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

Research background: Many authors are currently exploring the impact of the industry on the financial structure of enterprises since there are statistically significant differences across various sectors, exposing the industry as a critical factor influencing corporate indebtedness. Clusters of sectors with homogeneous patterns of indebtedness and comparable levels of debt in various economic conditions may be determined, and, therefore, firms and their debt levels should be systematically examined and evaluated. Purpose of the article: The main aim of this paper is to evaluate the level of debt in the Slovak environment on a sample of 19,480 firms from various sectors and to identify the relationships among them to identify sectors with homogeneous patterns of indebtedness and, consequently, comprehend which sectors are the most stable and independent. Methods: Because NACE classification provides a framework for gathering and presenting statistical data based on many economic sectors, the number of input data was reduced based on cluster analysis. Using Ward's hierarchical clustering method using squared Euclidean distance, selected indebtedness ratios were used to define the sectors with comparable debt levels. To determine if there were statistically significant differences between the calculated debt ratios related to the economic sector, the Kruskal-Wallis test was performed. Subsequently, as the results indicated significant differences across various indebtedness ratios, a post hoc analysis was performed. Findings & Value added: A group of NACE activities in which enterprises are sufficiently comparable that it is appropriate to evaluate their debt levels using the chosen indebtedness ratios are considered to be in sectors C, F, G and H, which included to tertiary sector, while the sectors K, R and S may also be grouped in one cluster, while form the secondary sector. The key relevance of our findings is the benchmarking of selected sectors about indebtedness, which may be used to further examine their growth in each of the V4 nations, which is an essential area for the evolution of the European economy as a whole. Studies considering a relatively significant amount of capital structure determinants may be beneficial to owners and managers, regulators, and financial institutions since debt policy affects firm performance, value, and survival.

Understanding Italian consumers' perceptions of tomato agricultural innovation: Exploring the nexus between sustainability, health and consumer beliefs

The growing emphasis on sustainability and health concerns has underscored the necessity for low-pesticide and safe foods, leading to increased adoption of organic production process and Integrated Pest Management (IPM). From this concept emerged the “Zero Residue" certification, while the “Nickel-free" claim serves health-related purposes. These attributes are related to consumer beliefs and, along with intrinsic quality cues such as colour, intervene in consumer decision-making process and result from technological and varietal agricultural innovation. They were evaluated alongside price to examine consumer preferences, using tomatoes as a case study. Conjoint Analysis and Ward's clustering method helped segregate Italian consumers into homogenous market segments based on their stated preferences. Organic production was the most significant attribute, followed by price, the “Nickel-free” claim, and colour. Thus, consumer demand increasingly favours environmentally sustainable and health-focused attributes. The “Zero Residue" concept offers an approach to reduce synthetic fertilizers and pesticides, appealing to environmental conscious consumers. Attributes like “Nickel-free" claim have emerged, which is important for consumers seeking innovative health features. Businesses should diversify products and employ clear labelling to inform consumers about eco-friendly practices. They should also offer competitive prices for innovative tomato products, thus implementing price segmentation may be necessary for diverse consumer segments. Policymakers can engage consumers through educational campaigns, enforce product labelling regulations highlighting health and environmental benefits, and support certification programs.

Selection and Yield Formation Characteristics of Dry Direct Seeding Rice in Northeast China.

Dry direct seeding rice (DSR) is an emerging production system because of increasing labor and water scarcity in rice cultivation. The limited availability of rice cultivars suitable for dry direct seeding hampers the widespread adoption of this cultivation method in Northeast China. This study aimed to investigate grain production and plant characteristics associated with dry direct seeding rice. We conducted a field experiment on 79 japonica rice cultivars in Shenyang City, Liaoning Province, Northeast China, in 2020 and 2021. This study found that the grain yield of the tested rice cultivars ranged from 5.75-11.00 t ha-1, with a growth duration lasting between 144-161 days across the cultivars. These cultivars were then categorized into high yielding (HY), medium yielding (MY), and low yielding (LY) based on daily yield by using Ward's hierarchical clustering method. The higher grain yield for HY compared to MY and LY was attributed to more spikelets per unit area. The HY alleviated the conflict between higher panicle density and larger panicle size by improving the seedling emergence rate and productive stem rate. It also significantly increased shoot biomass at maturity. The HY reduced the period between seeding and beginning of heading (BBCH 51) and the proportion of dry matter partitioned to the leaf at the heading stage. However, it also increased the accumulation of dry matter in the grain and the proportion of dry matter partitioned to the grain at maturity. Furthermore, the HY markedly increased the harvest index and grain-leaf ratio, which are beneficial to coordinate the source-sink relationship. A quadratic function predicted that 98 days is the optimum growth duration before heading (BBCH 51) for achieving maximum yield. In conclusion, for dry direct seeding rice, it is appropriate to select high-yielding japonica inbred rice cultivars with shorter growth duration before heading (about 93-102 day), higher panicle number (about 450-500 × 104 ha-1), more spikelet number per panicle (about 110-130), higher seedling emergence rate (about 70-75%), higher productive stem rate (about 60-70%), and greater harvest index (about 50-55%).

Algebraic Multi-Layer Network: Key Concepts.

The paper refers to interdisciplinary research in the areas of hierarchical cluster analysis of big data and ordering of primary data to detect objects in a color or in a grayscale image. To perform this on a limited domain of multidimensional data, an NP-hard problem of calculation of close to optimal piecewise constant data approximations with the smallest possible standard deviations or total squared errors (approximation errors) is solved. The solution is achieved by revisiting, modernizing, and combining classical Ward's clustering, split/merge, and K-means methods. The concepts of objects, images, and their elements (superpixels) are formalized as structures that are distinguishable from each other. The results of structuring and ordering the image data are presented to the user in two ways, as tabulated approximations of the image showing the available object hierarchies. For not only theoretical reasoning, but also for practical implementation, reversible calculations with pixel sets are performed easily, as with individual pixels in terms of Sleator-Tarjan Dynamic trees and cyclic graphs forming an Algebraic Multi-Layer Network (AMN). The detailing of the latter significantly distinguishes this paper from our prior works. The establishment of the invariance of detected objects with respect to changing the context of the image and its transformation into grayscale is also new.

Cluster analysis of carboniferous gas reservoirs and application of recovery prediction model

Since the discovery of the Carboniferous gas reservoirs in East Sichuan in 1977, after more than 40 years of development, most of the gas reservoirs have entered the middle and late stages of development. The gas reservoir is characterized by strong heterogeneity, large difference in permeability, and serious impact of water invasion in some blocks. Therefore, how to make a correct decision on gas field development and deployment is of vital importance. Combined with system clustering, BP neural network, correlation analysis and other methods, this paper first analyzes and calculates the static indicators of the Carboniferous gas reservoirs, and then divides the gas reservoirs into four categories using ward clustering method according to the calculated weight value, and determines the characteristics of each type of gas reservoirs using correlation coefficient analysis method. Finally, the recovery prediction model of each type of gas reservoir is established according to the BP neural network. The results indicate that: (1) The recovery rate prediction model can predict the trend of cumulative gas production changes, thereby obtaining the space for improving recovery rate, and the accuracy of the prediction results is high, which can be used as a reference for gas field planning. (2) The sub-active gas reservoirs with strong heterogeneous water bodies and the inactive gas reservoirs with low permeability water bodies have a certain space for enhanced oil recovery.

Metagenomic Surveillance in Jinan, China Reveals Serum Microbiome and Biochemistry Features in Fever of Unknown Origin (FUO) Patients.

Here we aim to build up a metagenomics-centered surveillance on the infectious microbiome showing in the fever of unknown origin (FUO) patients. We collected venous blood, bronchoalveolar lavage fluid, cerebrospinal fluid, tissue block, sputum, bone marrow biopsy, and purulent liquid samples from 123 patients. Metagenomic sequencing (mNGS) for both DNA and RNA sequences was performed to profile the total pathogenic microbiome in the samples. A large pool of infectious or conditional infectious bacteria were found, belonging to Enterobacteriaceae Staphylococcaceae (10.55%), Burkholderiaceae (10.05%), Comamonadaceae (4.25%). The major viruse families detected from mNGS analysis include Adenoviridae, Anelloviridae, Peribunyaviridae, Flaviviridae and Herpesviridae, showing in 34.96%, 47.37%, 30.89%, 5.69%, 3.25% and 1,63% patients, respectively. Using Ward clustering method, two clusters of patients were organized: high-variety group and low-variety group. The patients in high-variety group demonstrated higher levels of immune cells and inflammatory indicators such as lactate dehydrogenase, aspartate aminotransferase and alanine aminotransferase. The patients in the low-variety group showed higher levels of inflammatory lipids such as 13,14-dihy-15-keto PGE2 (fold > 10, p = 0.021); Tetra-PGDM (fold = 5.29, p = 0.037) and 20-HETE (fold > 10, p = 0.02). The mNGS surveillance system demonstrated remarkable potential in preventing infectious diseases using mNGS data.

Implementasi metode Ward clustering untuk klasterisasi penduduk yang memiliki jaminan kesehatan pada kabupaten dan kota di Provinsi Maluku

The Health Insurance Program is a protection guarantee for comprehensive health services which includes promotive, preventive and curative and rehabilitative services provided in stages for the community/participants whose contributions are paid by the government. The health insurance program in Maluku Province from year to year has been getting better. However, there are still disparities or gaps between regions in Maluku Province, so it is necessary to study the clusterization of the population who have health insurance in districts/cities in Maluku Province. Ward clustering method is a method that aims to get a cluster that has the smallest possible internal cluster variance. The purpose of this study is to obtain a cluster of residents who have health insurance in districts/cities in Maluku Province. The data used in this study were sourced from Maluku publications in figures at the Maluku Province BPS. This study obtained the results that there are 3 clusters in the cluster of residents who have health insurance in districts/cities in Maluku Province with details of cluster 1 consisting of Tanimbar Islands Regency, Central Maluku, Buru, West Seram, East Seram and South Buru District. Cluster 2 consists of Southeast Maluku Regency, Aru Islands, Southwest Maluku and Tual City. Furthermore, Cluster 3 consists of Ambon City.
 Keywords: Clustering, health insurance, wardMSC2020: 62H30

Characterization and cluster analyses of elderly asthma in comparison with nonelderly patients with asthma in Japan

BackgroundAsthma is a heterogeneous disease with multiple phenotypes that are useful in precision medicine. As the population ages, the elderly asthma (EA, aged ≥ 65 years) population is growing, and EA is now a major health problem worldwide. ObjectiveTo characterize EA and identify its phenotypes. MethodsIn adult patients with asthma (aged ≥ 18 years) who had been diagnosed with having asthma at least 1 year before study enrollment, 1925 were included in the NHOM-Asthma (registered in UMIN-CTR; UMIN000027776), and the data were used for this study, JFGE-Asthma (registered in UMIN-CTR; UMIN000036912). Data from EA and non-EA (NEA) groups were compared, and Ward's minimum-variance hierarchical clustering method and principal component analysis were performed. ResultsEA was characterized by older asthma onset, longer asthma duration and smoking history, more comorbidities, lower pulmonary function, less atopic, lower adherence, and more hospital admissions because of asthma. In contrast, the number of eosinophils, total immunoglobulin E level, oral corticosteroid use, and asthma control questionnaire scores were equivalent between EA and NEA. There were 3 distinct phenotypes in EA, which are as follows: EA1: youngest, late onset, short duration, mild; EA2: early onset, long duration, atopic, low lung function, moderate; and EA3: oldest, eosinophilic, overweight, low lung function, most severe. The classification factors of the EA phenotypes included the age of onset and asthma control questionnaire—6. Similarities were observed between EA and NEA phenotypes after principal component analysis. ConclusionThe EA in Japan may be unique because of the population's high longevity. Characterization of EA phenotypes from the present cohort indicated the need for distinct precision medicine for EA. Trial RegistrationJFGE-Asthma registered in UMIN-CTR (https://www.umin.ac.jp/ctr/); UMIN000036912.

Personality Type and Chronic Pain: The Relationship between Personality Profile and Chronic Low Back Pain Using Eysenck's Personality Inventory.

Personality type plays a key role in how individuals respond to a variety of stimuli; however, it is unclear if there is a significant influence on pain perception. While pain is associated with many conditions, chronic low back pain (cLBP) is one of the most prevalent and debilitating problems in modern society. Treating this condition can be a challenge and clinicians must understand all factors that can influence pain perception. The present study investigated the relationship between personality type and pain experience in patients experiencing cLBP. One hundred twenty-four participants completed the Eysenck Personality Inventory (EPI), which identifies two major components of the human personality, neuroticism, and extraversion. Participants also completed the Oswestry Disability Index (ODI), the Tampa Scale for Kinesiophobia (TSK), the Numeric Pain Rating Scale (NPRS), and the Pain Catastrophizing Scale (PCS). The association between pain and personality was determined with a Spearman Rank Correlation Coefficient. A hierarchical cluster analysis with Ward's clustering method examined for subgroups of individuals based on these variables. The neuroticism score (EPI-N) was found to have a statistically significant relationship with all pain outcome measures. This suggests that people exhibiting a neurotic personality type are likely to have more fear of movement (p = 0.001), greater catastrophizing behavior (p < 0.001), higher self-reported levels of disability (p < 0.001), and higher overall reported levels of pain (p = 0.046) than those with other, more stable personality types. Three clusters were derived with varying levels of pain-related factors and personality. Personality type appears to have an influence on many of the attributes associated with cLBP and may be a useful determinate in both prognosis and interventions.

Classifications of moderate to severe asthma phenotypes in Japan and analysis of serum biomarkers: A Nationwide Cohort Study in Japan (NHOM Asthma Study)

BackgroundAsthma is a heterogeneous disease, and phenotyping can facilitate understanding of disease pathogenesis and direct appropriate asthma treatment. This nationwide cohort study aimed to phenotype asthma patients in Japan and identify potential biomarkers to classify the phenotypes. MethodsAdult asthma patients (n = 1925) from 27 national hospitals in Japan were enrolled and divided into Global Initiative for Asthma (GINA) steps 4 or 5 (GINA 4, 5) and GINA Steps 1, 2, or 3 (GINA 1–3) for therapy. Clinical data and questionnaires were collected. Biomarker levels among GINA 4, 5 patients were measured. Ward's minimum variance hierarchical clustering method and tree analysis were performed for phenotyping. Analysis of variance, the Kruskal–Wallis, and chi-square tests were used to compare cluster differences. ResultsThe following five clusters were identified: 1) late-onset, old, less-atopic; 2) late-onset, old, eosinophilic, low FEV1; 3) early-onset, long-duration, atopic, poorly controlled; 4) early-onset, young, female-dominant, atopic; and 5) female-dominant, T1/T2-mixed, most severe. Age of onset, disease duration, blood eosinophils and neutrophils, asthma control questionnaire Sum 6, number of controllers, FEV1, body mass index (BMI), and hypertension were the phenotype-classifying variables determined by tree analysis that assigned 79.5% to the appropriate cluster. Among the cytokines measured, IL-1RA, YKL40/CHI3L1, IP-10/CXCL10, RANTES/CCL5, and TIMP-1 were useful biomarkers for classifying GINA 4, 5 phenotypes. ConclusionsFive distinct phenotypes were identified for moderate to severe asthma and may be classified using clinical and molecular variables (Registered in UMIN-CTR; UMIN000027776.)

Can unsupervised learning methods applied to milk recording big data provide new insights into dairy cow health?

Among the dairy sector's current concerns, the assessment of global animal health status is a complex challenge. Its multidimensionality means that global monitoring tools are rarely considered. Instead, specific disease detection is often studied separately and, due to financial and ethical issues, uses small-scale data sets focusing on few biomarkers. Several studies have already been conducted using milk Fourier transform mid-infrared (FT-MIR) spectroscopy to detect mastitis and lameness or to quantify health-related biomarkers in milk or blood. Those studies are relevant but they focus mainly on one biomarker or disease. To solve this issue and the small-scale data set, in this study, we proposed a holistic approach using big data obtained from milk recording, including milk yield, somatic cell count, and 27 FT-MIR-based predictors related to milk composition and animal health status. Using 740,454 records collected from 114,536 first-parity Holstein cows in southern Belgium, we performed repeated unsupervised learning algorithms based on Ward's agglomerative hierarchical clustering method to find potential interesting patterns. A divide-and-conquer approach was used to overcome the limitation of computational resources in clustering a relatively large data set. Five groups of records were identified. Differences observed in the fourth group suggested a relationship to metabolic disorders. The fifth group seemed to be related to mastitis. In a second step, we performed a partial least squares discriminant analysis (PLS-DA) to predict the probability of belonging to those specific groups for the entire data set. The obtained global accuracy was 0.77 and the balanced accuracy (i.e., the mean between sensitivity and specificity) of discriminating the fourth and fifth groups was 0.88 and 0.96, respectively. Then, a validation of the interpretation of those groups was performed using 204 milk and blood reference records. The predicted probability associated with the metabolic disorders issue had significant correlations of 0.54 with blood β-hydroxybutyrate, 0.44 with blood nonesterified fatty acids, -0.32 with blood glucose, -0.23 with milk glucose-6-phosphate, and 0.38 with milk isocitrate. In contrast, the predicted probability of belonging to the mastitis group had correlations of 0.69 with milk lactate dehydrogenase, 0.46 with milk N-acetyl-β-d-glucosaminidase, -0.18 with milk free glucose, and 0.16 with milk glucose-6-phosphate. Consequently, these results suggest that the obtained quantitative traits indirectly reflect some of the main health disorders in dairy farming and could be used to monitor dairy cows on a large scale. By using unsupervised learning on large-scale milk recording data and then validating the pattern using reference laboratory measures, we propose a new approach to quickly assess dairy cow health status.

МОДЕЛІ ФІНАНСУВАННЯ ТА ЯКІСТЬ ВИЩОЇ ОСВІТИ В КРАЇНАХ ОЕСР

The analysis of the interrelationship of expenditures on higher education and the quality of higher education with higher education funding models in OECD countries and their cluster distribution, based on the described criteria, is done in the article. Two models of higher education funding are characterized: one is based on the source of financing and the possibility of influence of educational institutions on the system of higher education (proposed by V. Praneviciene), the other is based on the source of financing and the level of state regulation (proposed by O. Kuklin). The grouping of OECD countries was carried out based on the criteria of the amount of higher education funding per student and the quality indicator of higher education according to the U21 rating using IBM SPSS Statistics based on the Ward clustering method using the square of the Euclidean distance, the standardization of variables was carried out on the basis of Z-scores. As a result of the grouping of 23 countries, 5 clusters were formed, and the first cluster with the highest level of expenditure per student and the level of quality of higher education according to the U21 rating is represented by one country - the USA. A matrix of the relationship of higher education funding models in OECD countries and their cluster distribution based on the described criteria of the amount of expenditures and the quality of higher education was formed. Based on the analysis of the matrix, it was found that in the USA the market model is used for both classifications. In this case, it may be explained by the high level of socio-economic development of the country: due to the high level of GDP, it is possible to finance higher education mainly at the expense of private sources, and the state as a whole provides support for students who are unable to pay for education. In the countries of the second and third clusters, bureaucratic or anti-market models are mainly used. In the countries of the fourth and fifth clusters, there is a practice of using different models.

Essential oils of Eplingiella fruticosa populations: chemical, antioxidant, and cytotoxic analyses

This study investigates the variations in the chemical profiles and biological activities (antioxidant and cytotoxic) of Eplingiella fruticosa from the state of Sergipe, an endemic species from the Northeast region of Brazil. The essential oils were extracted from six populations by hydrodistillation and analyzed by GC/MS-FID. Cluster analysis was performed with the data of the constituents of the essential oils, and then a dissimilarity matrix, based on Euclidean distances, and a dendrogram, through the Ward clustering method, were constructed. The antioxidant activity of the essential oils was tested by different assays (DPPH, ABTS, β-carotene, and FRAP), and cytotoxic activity was tested by the SRB assay. The compounds found in greater amounts were α-pinene, β-pinene, 1,8-cineole, camphor, borneol, δ-elemene, α-cubebene, α-ylangene, (E)-caryophyllene, germacrene D, bicyclogermacrene, trans-calamenene, spathulenol, caryophyllene oxide, and viridiflorol. These compounds defined the formation of two groups. The first group was composed of the populations of São Cristóvão, Itaporanga, Japaratuba, and Malhada dos Bois municipalities and was characterized by the presence of the monoterpene camphor (8.39-11.27%) as the compound of greatest concentration in relation to the other municipal areas. The second group was composed of the populations of Moita Bonita and Pirambu municipalities and was characterized by the major presence of the sesquiterpene bicyclogermacrene (7.45% and 10.98%). The plants exhibited weak effects in terms of antioxidant activity; however, the essential oil showed significant toxicity for the lines A549 (51.00% cell viability) in the population of Japaratuba, and B16F10 (64.94% cell viability) in Malhada dos Bois. The observations of this study may open a way to optimize the use of the E. fruticosa populations in relation to their cytotoxic properties.

بررسی ارزش صفات گل‌آذین و دانه در آرایه‌شناسی جنس تاج‌خروس در ایران

The genus Amaranthus, with 14 native or cultivated species (total 17 species and subspecies), is distributed in different regions of Iran. Of these, 10 are known as weedy taxa. In the present study, we selected and analyzed the morphological and micromorphological characteristics of the inflorescence and seeds in Iranian Amaranthus taxa to investigate the diversity and to clarify the relationships among them. Scanning electron microscope (SEM) micrographs of seeds of 16 taxa were presented. In order to determine the similarity of the studied taxa and their classification in relation to the measured characteristics, cluster analysis was used by Ward method. The evaluation of data by WARD clustering and PCA biplot classification methods showed similar results. The results divided the taxa into three main groups, which do not fully correspond to the sections mentioned in the classification of this genus; however, confirm somewhat the close relationships of the taxa. . .

Atmospheric circulation regimes for prescribed burns along the U.S. Gulf of Mexico coast

The regional-to-synoptic-scale steering atmospheric circulation is important for advecting energy, mass, and momentum across space, with the near-surface wind being the local manifestation of this circulation. The local winds can affect phenomena of economic, ecological, and/or aesthetic values, such as bird migrations and prescribed burns to restore bird habitats. The purpose of this research is to classify the days from 1979 to 2018 based on circulation patterns near the surface (i.e., 1000-hPa) and at the highest level of airflow affecting bird migration (i.e., 700-hPa), across the Gulf Coast region of the United States. The resulting patterns, derived from Ward's clustering method on rotated principal component scores, are used as input in a fire management plan to restore niches for bird species such as black and yellow rails and mottled ducks. Results suggest that for the (near-surface) 1000-hPa level, a classification system consisting of eight circulation types maximizes the between-group variability while minimizing the within-group variability, with a total of 95.8 percent of the dataset variance explained, and reasonable correspondence with existing manual, subjective and semi-automated techniques. At the 700-hPa level, a system of eight circulation types was derived, explaining 95.05 percent of the dataset variance. In general, the western U.S. Gulf of Mexico arc is more likely than the eastern to respond to synoptic control in the form of weather types, while the eastern region may have more influence from local-to-meso-scale land and sea breezes. These results will assist in optimizing the success of controlled burn conditions while enhancing efforts to minimize advection to populated areas.

Madograms help to quantify mountain frontal zones—An approach towards comparative spatial analysis of complex landforms

AbstractThis article explores the value of madograms to characterize the geomorphology of frontal belts of mountain ranges. Madograms are a geostatistical, variogram‐related concept, allowing for the analysis of altitude variability as a function of distance. After simple idealized digital elevation models were tested to reveal characteristic madogram shapes for various landform configurations, madograms were calculated for three frontal zones of different origin and morphology, located in the Sudetes range (Central Europe). Isopleth madograms and Ward's clustering method were applied to investigate similarities and differences within and between frontal zones. The results proved consistent with the accumulated, largely qualitative knowledge about geomorphology of the frontal zones and confirm the usefulness of the method, which allows for more insightful quantification of the morphology of mountain frontal zones, recognition of their internal spatial structure, and discrimination between fronts of various morphology and origin. Various recommendations for future users are also offered.

Health literacy profiling in persons with psoriasis - A cluster analysis.

ObjectiveTo explore health literacy (HL) profiles within a cohort of people with psoriasis. A cluster approach identifies groups of individuals that have similar HL profiles. The method unmasks sub‐groups with particular HL strengths, or subgroups with limitations, which require tailored healthcare services to improve.MethodsA cross‐sectional sample of 792 patients from the Norwegian Climate Helio Therapy Programme in Gran Canaria participated. The HL questionnaire assessed nine HL dimensions. Using Ward's Hierarchical Clustering Method (Stata version 16), we looked for subgroups of patients across the dimensions. We also explored whether these clusters had specific demographic features and associations to outcomes such as psoriasis knowledge, quality of life and self‐management capacity.ResultThe analysis revealed four unique clusters identifying clinically meaningful subgroups. Two groups stood out as especially interesting. One cluster representing 26.6% of the sample presented severe HL limitations associated with lower psoriasis knowledge, quality of life, self‐management and self‐efficacy. HL domains connected to cooperation with healthcare professionals showed deficient scores. The other cluster included a smaller percentage (7.7%) with high HL compared to the total sample. This cluster was associated with higher self‐management, quality of life and better self‐efficacy.ConclusionThe cluster analysis revealed substantial differences in HL profiles within the sample. These results support the importance of a holistic understanding of the HL needs and the vulnerabilities within a psoriasis cohort. Implementing one size fits all approaches, may not be sufficient in psoriasis context to target HL.