Agglomerative Hierarchical Clustering Research Articles

Typology analysis of cattle farms in Northeast Algeria: Potential for sustainable development

CONTEXTCurrently, local cattle breeds are facing numerous challenges, and their disappearance could have social, economic, and environmental consequences. OBJECTIVEThis study was conducted in the northeast of Algeria to understand the characteristics, constraints of production systems and examine the specific strategies implemented in farms to ensure their viability. METHODA total of 175 smallholder farmers who practice Algerian local cattle breeding were interviewed using a structured questionnaire. Typology construction was carried out through factor analysis of mixed data, followed by sequential agglomerative hierarchical and K-means clustering, to define distinct farmer types with common characteristics. RESULTS AND CONCLUSIONResults highlight three farm types. The first group, representing 54.9% (96/175Farms) of the farms studied, is characterized by low-resource breeders who raise small herds of local breeds in association with small ruminants. Type 2 (25.7%) (45/175) consists of crossbred, diversified, supported breeding with better technical performance. The intensification strategies adopted by breeders of this type have relegated local cattle to the background. The third type (19.4%) (34/175) includes cattle pastoral farms, with the predominance of the local cattle breed characterized by limited productivity. SIGNIFICANCEThis study highlights farm diversity, the necessity of government support, and specific policies to ensure the sustainability of local cattle breeding, with particular emphasis on pastoral breeding, which has the largest number of local cattle breeds.

Efficient and Simplified Modeling for Kerosene Processing Quality Detection Using Partial Least Squares-Discriminant Analysis Regression

Kerosene from various refineries and crudes is used for heating and other purposes in many countries like Iraq; therefore, it is important to identify its source to recognize and tax any adulteration. In this study, a fast classification technique for kerosene marketed in Iraq was developed with the goal of identifying its quality. The samples were categorized using a supervised partial least squares discriminant analysis (PLS-DA) approach. Multivariate analyses using agglomerative hierarchal clustering and principal component analysis were utilized to identify outliers and sample dissimilarities. The dataset was divided into calibration and prediction sets. The prediction set was used to evaluate the model’s separation performance. The Q2 cross-validation was applied. The PLS-DA models achieved significant accuracy, sensitivity, and specificity, showing strong segregation ability, notably for the calibration set (100% accuracy and 1.00 sensitivity). It was found that kerosene processing can be classified rapidly and non-destructively without the need for complicated analyses, demonstrating the best results for classification even when compared with the classification outcomes of other fuels. This PLS-DA approach has never been looked at before for process quality detection, and the results are comparable to direct kerosene classification with soft independent modeling of class analogy and support vector machines.

Variability of Silver Fir Needle (Abies alba Mill.) Anatomical Features in the Southeast Europe Natural Populations

The survival of marginal/peripheral silver fir (Abies alba Mill.) populations in the broader region of Southeast Europe is endangered due to climate change and population decline. This study aimed to determine the level and pattern of variability for the anatomical traits of needles and the possibility of linking the pattern of phenotypic variability with environmental factors. In most of the analyzed needle traits, the statistically significant variability between populations was determined. According to the results of the multivariate principal component analysis, it is evident that the populations are distinct from each other, in three groups. The climatic factors Hargreaves reference evaporation, mean annual temperature, and growing degree-days, were statistically significantly correlated. The altitude and heating degree-days are statistically significantly correlated with the following three environmental factors: Hargreaves reference evaporation, mean annual temperature and growing degree-days, but negatively with others. The paper’s findings indicate significant moderate and high correlations between the anatomical traits of the needles’ central bundle diameter with the resin duct diameter, the distance between the vascular bundle and the resin duct and the epidermis thickness with cuticle, the resin duct diameter with the distance between the vascular bundle and the resin duct and the epidermis thickness with cuticle, as well as the distance between the vascular bundle and the resin duct with the hypodermis height and the epidermis thickness with cuticle. The results of agglomerative hierarchical clustering analysis, performed for anatomical and climatic traits, confirmed the existence of three groups of tested populations according to the altitude gradient. Research results provide knowledge on the diversity and structure of Abies alba populations of Southeast Europe, important for further research and guidelines for the species’ conservation and genetic variability preservation in the southern marginal distribution area and keeping in line with climate change projections.

Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

Abstract PO1-07-01: Artificial intelligence-based prediction of Oncotype DX Score from whole slide images using human-interpretable features and breast biomarkers

Abstract Background The Oncotype DX Breast Recurrence Score assay (ODX) is a commonly used genomic test for patients with estrogen receptor (ER)-positive, HER2-negative, early-stage invasive breast cancer. While ODX predicts patients’ recurrence risk and benefit from chemotherapy, it is tissue and time-consuming, and expensive. Previous deep-learning or non-linear ODX prediction models achieved promising performance using whole-slide images (WSI) or with other covariates (e.g., ER, progesterone receptor (PR), HER-2, Ki-67 scores and tumor stage) but systematic quantification of the contribution of individual histological features to the ODX score remained challenging. Here, we extracted a rich set of human-interpretable features (HIFs) quantifying nuclear morphology and the distribution of cells and tissues in the tumor microenvironment. We used these HIFs, along with manually assessed ER, PR, HER-2 scores, and tumor stage, to predict ODX scores. We also explored the role of Ki-67 features in augmenting our model predictions Methods We developed machine learning models to extract cell, tissue, and nuclear HIFs from WSI stained with hematoxylin and eosin (H&E) and immunohistochemistry (IHC) against Ki-67. These models were deployed on 353 H&E WSI to extract 276 HIFs quantifying cell densities, tissue areas, relative cell counts, and nuclei size, shape and color. Hierarchical agglomerative clustering was applied to cluster these features into 11 groups. Univariate regression was performed to identify correlations between ODX scores and feature clusters. To predict ODX score using HIFs, a multivariable regression model was fitted to the data where regressors are representative features from each cluster, in addition to ER, PR, HER-2 scores and stage as covariates. The model was fitted on the training set (N=266 slides) and evaluated on the held-out test set (N=87 slides). To examine the utility of Ki-67 IHC features in ODX prediction, a Ki-67 model was deployed on a subset of 194 matched Ki-67 IHC WSI to extract count proportions and densities of Ki-67 positive cells in cancer epithelium and stroma. Results Significant positive correlations were identified between ODX scores and feature clusters corresponding to cancer cell density (p < 10-7), macrophage density (p < 10-4), immune cell density (p < 10-16), and variations in cancer nuclear size (p < 10-5) and color (p < 10-3). Significant negative correlations were observed between ODX scores and clusters related to fibroblast density (p < 10-3), and variations of non-cancer cell nuclear color (p = 0.02). Evaluation of our model’s ability to predict ODX scores revealed an association between predicted and observed scores (Pearson r = 0.74). The AUROC for model predictions of high/low classifications (with reference to a cutoff ODX score of 20) was 0.80. Ki-67 features were clustered together with H&E cancer cell density features and showed a stronger correlation with ODX scores compared to the H&E features alone. Combining Ki-67 cell density features with H&E features led to increased performance of the multivariable regression model on the test set compared to H&E features only (r = 0.62 vs. r = 0.59). However, a model combining Ki-67, H&E and covariates performed similarly to a model with H&E and covariates (r = 0.73), suggesting that Ki-67 features may not be needed for ODX prediction when H&E and covariate information is available. Conclusions Our model predicts ODX recurrence scores with comparable performance to other black-box approaches using WSI and breast biomarker status and without the need for, Ki-67 features. Using readily available information, our model has the potential to be a convenient screening tool for patient stratification, which may lead to better patient care, lower costs, and faster treatment. Citation Format: Nhat Le, John Van Arnam, Christian Kirkup, Ylaine Gerardin, Michael Drage, Neha Khaitan, Anurag Sharma, Georgi Galev, Nicholas Indorf, Emily Sansevere, Ilan Wapinski, Stephanie Hennek, Gloria Zhang. Artificial intelligence-based prediction of Oncotype DX Score from whole slide images using human-interpretable features and breast biomarkers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-01.

Tracking dry gin volatile organic compounds over distillation: a time course study

Why was the work done: The influence of distillation time on the volatile composition of gin has not been examined in detail at a commercial distillery. How was the work done: Headspace Solid-Phase Micro-Extraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) was used to tentatively identify 74 aroma compounds, with their concentration tracked in distillate samples over the course of three gin distillations. What are the main findings: Four unique fractions were identified using Agglomerative Hierarchical Clustering: ‘heads’, ‘early hearts’, ‘late hearts, and ‘tails’. The hearts fraction (n=24 samples) was examined further, fitting statistically appropriate models to 54 aroma volatiles. Three sub-groups of volatiles were identified, (i) highly volatile monoterpenes with rapidly decreasing concentration over the early hearts fraction, (ii) volatiles whose concentration increased gradually over the hearts fraction and (iii) less volatile sesquiterpenes, sesquiterpenoids, and monoterpenoids which increased rapidly in concentration in the later distillate. Complex cubic models fit 34 volatiles with very high significance (p>0.0001) over the hearts fraction (72%). Informal aroma sensory bench testing identified distinct aroma categories with, for example, ‘Spicy’ being commonly detected in the later distillate. Why is the work important: This investigation characterises the kinetics of flavour extraction over the course of a commercial gin distillation process. These findings highlight the potential for the expression of specific flavour characteristics by modifying the cut points of the distillation process

Abstract PO4-01-05: The prognostic importance of biological pathway enrichment patterns in trastuzumab-treated early HER2-positive breast cancer

Abstract Background: Biological tumor signatures of HER2-positive breast cancer may impact patients’ response to HER2 targeted treatment and are important to investigate since there is a need for both escalation and de-escalation of HER2-directed treatments. We aimed to examine subgroups of HER2-positive tumors based on different biological pathway enrichment patterns and their prognostic value. Materials and methods: In a retrospective cohort of 388 early HER2-positive breast cancer patients treated with adjuvant trastuzumab, 356 HER2-positive FFPE samples were successfully analyzed for gene expression patterns using the Human nCounter Breast Cancer 360 panel (Nanostring Technologies). The raw data underwent extensive quality control and normalization in the nSolver software v.4.0 and its Advanced Analysis Module v.2.0 (NanoString Technologies). Unsupervised hierarchical agglomerative clustering was performed on pathway enrichment of nSolver customized biological process. Survival analysis for recurrence-free survival of pathway-driven clustered patients was examined using Kaplan-Meier and multivariable Cox regression models. Results: Four subgroups of patients with different biological pathway enrichment patterns were observed. The subgroup with the combination of low stromal and low immune biological pathways (Low_stromal/Low_immune) had a significantly worse prognosis than the High_stromal/Low_immune subgroup (RFS HR: 0.4; CI:0.2-0.9, p=0.02) and Low_stromal/High_immune subgroup (RFS HR: 0.4; CI:0.2-0.9, p=0.03). In addition, the subgroup with upregulation of both stromal and immune pathways (High_stromal/High_immune) showed a better response to trastuzumab than the Low_stromal/Low_immune subgroup even if not statistically significant (HR: 0.4; CI:0.2-1.0, p=0.06). Moreover, the clusters of upregulated immune pathways showed the highest levels of tumor infiltrating lymphocytes. Conclusions: In this trastuzumab-treated early HER2-positive breast cancer cohort, we observed four subgroups with different biological pathway enrichment patterns. The subgroup with the combination of low stromal and low immune biological pathway enrichment was associated with a poorer prognosis than those mainly enriched with either stromal or immune pathways. This study shows the diversity of biological signatures within HER2-positive breast cancer, and results could potentially be used in future studies of HER2 target treatments. External validation is motivated. Citation Format: Caroline Rönnlund, Qiao Yang, Caroline Schagerholm, Theodoros Foukakis, Irma Fredriksson, Xinsong Chen, Stephanie Robertson, Emmanouil Sifakis, Johan Hartman. The prognostic importance of biological pathway enrichment patterns in trastuzumab-treated early HER2-positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-01-05.

Effect of environmental conditions and seasons on the growth, yield, and essential oil composition of Foeniculum vulgare L.

Foeniculum vulgare (Fennel) oils have an essential function in the food and therapeutic industries. The study’s purpose was to describe how different environmental circumstances and locations in Egypt affected the fennel oil yields and compositions. To assess the growth, yield, and essential oil constituents of F. vulgare at four differing agro-ecological regions in Egypt (El-Minia Governorate, Bahariya Oasis, El-Sharkia Governorate, and Aswan Governorate) at two different planting dates, in-field study was performed throughout the seasons 2019/2020 and 2020/2021. According to the data, there are highly significant differences between locations and sowing dates; the second date (15th November) is the best date for planting fennel plants. Plants grown in the Bahariya Oasis location had the maximum oil yield in mL/plant and L/acre, whereas the Aswan location had the highest percentage of EO. Eight EO samples underwent GC-MS analysis and chemometric analysis, including principal component analysis (PCA) and agglomerative hierarchical clustering (AHC), yielding identification of a total of 22 compounds. EOs from plants grown in various environments varied significantly in terms of their chemical composition, mainly components‘ concentrations. The main components were identified as estragole (66.62-89.73%), D-limonene (5.76-17.04%), and fenchone (2.02-9.23%). In samples gathered on November 15, 2020-2021, at the Bahariya Sekem Farm, anethole was found at a high concentration (12.79%), despite the compound’s absence or minimal presence in the other EOs. According to the current study findings, modulation of the cultivation and ecological circumstances can be used for the improvement of funnel EO.

Evaluating visitors’ experience in museum: Comparing artificial intelligence and multi-partitioned analysis

Analysing visitors' behaviour in a museum or in a cultural site is a crucial element to manage spaces and artworks arrangement as well as improving the visit experience. This paper presents the preliminary results of the ARTEMISIA project, exploiting Artificial Intelligence (AI) techniques to study, design and develop a methodology to interpret visitors' behaviour within a museum context, namely the Museum of Rome in Palazzo Braschi (Rome, Italy). The aim is to combine literature on users' experience (UX) analysis with experimental data coming from the visitor anonymous tracking out of motion sensors (users' stand-still positions, viewpoint direction, movements), merging approaches of different research domains. Through the use of agglomerative hierarchical clustering algorithms, four categories of visitors were identified, then associated to user profiles emerged by UX evaluations. Such analysis may lead to new forms of visitors profiling and to the development of a new generation of customised applications in public and private contexts. Identifying and predicting users’ patterns with respect to museum halls arrangement may also be useful to suggest improvement in the museum spaces and exhibitions (new indications, updated storytelling or changes in thematic configuration).© 2023 Elsevier Ltd. All rights reserved.

Effects of banqueting on water infiltration and physico-chemical properties of soil in semi-arid lands

Banqueting is a simple and cheap soil conservation practice in natural rangelands under dry climates. Little research exists about its impacts compared to other land uses, where agriculture and land abandonment may be important reasons for degradation. This study has evaluated water infiltration and key physico-chemical properties of soil in natural rangelands with banquets in West Azerbaijan (North-Western Iran) compared to: (i) undisturbed sites (assumed as reference condition) and (ii) cultivated or abandoned dry farmlands. Under these soil conditions, the texture, bulk density, porosity, water content, pH, electrical conductivity, organic carbon and carbonate contents of soil (using common physical-chemical methods) as well as the hydraulic conductivity (using a single-cylinder infiltrometer) were measured in the topsoil (0–20 cm). This data was further processed by applying the Principal Component Analysis and the Agglomerative Hierarchical Cluster Analysis. In areas with banquets, soil hydraulic conductivity was higher by 84% compared to dry farmlands (abandoned or not), where water infiltration was noticeably reduced (−44%) compared to the reference soil condition. Moreover, a noticeable increase in organic carbon content (+80%) and a lower salinity (−36%) of soil compared to cultivated areas was measured. These effects are important to enhance soil fertility on one side, and resistance to erosion on the other side. Therefore, in the experimental areas, banqueting may counteract the degradation of soil due to the agricultural activity as well as its abandonment, and restore the properties of the undisturbed soils that are typical of natural rangelands. The results of this study may be of help for landscape managers and agronomists for the pressing needs for soil conservation and productivity in semi-arid areas that are prone to degradation and abandonment.

Identifying daily water consumption patterns based on K-means Clustering, Agglomerative Hierarchical Clustering, and Spectral Clustering algorithms

Abstract Understanding daily water consumption patterns is crucial for efficient management and distribution of water resources, as well as for promoting energy conservation and achieving carbon peaking and neutrality targets. It compares performance of three clustering algorithms, K-means Clustering (KC), Agglomerative Hierarchical Clustering (AHC), and Spectral Clustering (SC), using Silhouette Coefficient Index (SCI) and Calinski–Harabasz Index (CHI) as evaluation metrics. We conducted a case study using original hourly flow series of a water distribution division. It aims to identify typical daily water consumption patterns and explore factors that influence them. Findings are as follows: (1) among the three algorithms, KC demonstrates the best, with SCI of 0.6315, 0.5922, and 0.6272, and CHI of 305.9207, 274.1120, and 302.4738 for KC, AHC, and SC, respectively. (2) KC successfully identifies three distinct typical daily water consumption patterns. (3) Results indicate a significant impact of seasons on daily water consumption patterns. (4) Conversely, weekdays and holidays have minimal effect on daily water consumption patterns. It highlights the importance of comprehending daily water consumption patterns and underscores the effectiveness of KC in identifying such patterns. Furthermore, it emphasizes the significant influence of seasons while revealing limited impact of weekdays and holidays on daily water consumption patterns.

Genetic diversity analysis and molecular characterization of tropical rose (Rosa spp.) varieties

Characterization of rose varieties becomes essential for understanding, utilizing, and preserving the diversity among them, for their genetic improvement, cultivation, and conservation. With this purpose, 19 tropical rose varieties were evaluated via quantitative morphological and molecular markers. A significant degree of variation was observed across nearly all descriptors, suggesting a notable level of genetic diversity among the released rose varieties. Traits like flower diameter, flower yield/plot, and shelf life, considered to be of vital importance given the aesthetic value of rose, exhibited remarkable differences that would further aid in the selection of desirable varieties. The maximum flower diameter, flower yield/plot, and shelf life were recorded in Yellow Page, Lambada, and Delhi Princess, respectively. The varieties were evaluated by correlation matrix, hierarchical agglomerative clustering method-based dendrogram, network plot on phenotypic correlation, colour intensity graph including Red-Green-Blue (RGB) scattered plot. In the correlation matrix, it was found that more number of primary shoot contributes to higher flower yield, while increase in leaf area degraded the shelf life of the flower. Realizing the importance of molecular characterization, 10 selected polymorphic inter simple sequence repeats (ISSR) and start codon targeted (SCoT) polymorphism primers were tested that amplified total 187 polymorphic loci with 94.4 % polymorphism. Cent percent polymorphism were found with SCoT-2, SCoT-18, SCoT-19, HB-12 and ISSCR3 primers. A principal component analysis based on molecular diversity highlighted that First Red, Local Selection 2, and Himroz exhibited distinct characteristics compared to the other varieties. The 19 varieties were grouped into 8 clusters viaunweighted pair group method with arithmetic mean-based dendrogram at 0.4 similarity co-efficient. The analysis revealed a significant genetic distance between Himroz, First Red, and Grand Gala, suggesting their potential to be utilized in hybridization programs for crop improvement. The distinguishable genetic background and high degree of variation revealed by molecular markers, specifically ISSR and SCoT, offer valuable insights that can contribute to the strategic improvement of roses. These findings provide a foundation for expediting rose breeding programs and comprehensive crop improvement strategies.

Impacts of flow regime characterization on selected water quality parameters in Mthatha River catchment

Abstract The effects of altered streamflow types on selected water quality parameters provide useful knowledge and information for prompt water pollution management. This study assesses the impacts of inter- and intra-year seasonal variability in streamflow types on selected water quality parameters. The study uses percentiles, multivariance correlation, and agglomerative hierarchical clustering analysis (AHC) in XLSTAT statistics software to achieve the study aim. Historical dataset for streamflow, rainfall, and selected water quality parameters between 2012 and 2020 for the Mthatha River catchment (MRC), Eastern Cape, South Africa were used to characterize the different seasonal variability impacts with the selected water quality parameters at both the upper and lower reach of the river. The result of the inter and intra-year seasonal variability analyses show that the catchment's flow magnitude varies between 2.0 and 2.7 m3/s in summer while the results of the nonlinear polynomial relationship between the grouped streamflow regime show a coefficient of correlation (R2) values of 0.4264 and 0.5643, suggesting that a mild alteration occurs in the catchment. Decision-makers would find the study useful in controlling the pollutant effluent discharge into the river at different times and maintaining standard effluent requirements as mitigation strategies for sustaining the river ecosystem.

An Analysis of Meteorological Data in Sumatra and Nearby using Agglomerative Clustering

Sumatra is one of the biggest and the second most crowded islands in Indonesia. Sumatra is also a place of abundance of tropical flora and fauna. This paper aims to cluster the cities in Sumatra and nearby based on the meteorology data. It implements Agglomerative hierarchical clustering and uses a daily time series dataset from 17 cities from 1 January 2010 to 31 December 2023. The dataset contains variables minimum temperature, maximum temperature, average temperature, humidity, sunshine duration, and average wind speed. The preprocessing data was dedicated to managing the missing values and data aggregation to create single-form data. The single-form data contains cities and meteorological variables used as an input for the clustering algorithm, i.e. K-Means, Fuzzy C-Means, K-Medoid, intelligent K-KMeans, and Agglomerative clustering. The Agglomerative clustering outperforms other methods (i.e. K-Means, Fuzzy C-Means, K-Medoid, and intelligent K-KMeans) and produces Silhouette scores of 0.11. The clusters are then analyzed to find their unique pattern. The cut-off when the number cluster is two, Agglomerative hierarchical clustering gathers Aceh, Sabang, Pekanbaru, Padang, and Padang Lawas in Cluster 1. Other cities, i.e., Nagan Raya, Batam, Jambi, Bandar Lampung, Medan, Pangkalpinang, Palembang, Bengkulu, Belitung, Tapanuli, Deli Serdang, and Nias are in Cluster 2. The results can be briefly explained that the characteristic of Cluster 1 has a higher average temperature, lower humidity, and lower sunshine duration than cities in Cluster 2. However, Cluster 1 has a lower average minimum temperature than Cluster 2. The pairs of cities which have the most similarities are (Aceh, Sabang), (Pekanbaru, Padang Lawas), (Nagan Raya, Nias), (Jambi, Palembang), (Bengkulu, Tapanuli), and (Medan, Deli Serdang). The annual trend in several cities shows that there exists an increasing trend in minimum temperature, rising sunshine duration, and decreasing wind speed. These are signs of climate change that need a proper handling.

Unreliability of clustering results in sensory studies and a strategy to address the issue

Researchers commonly use hierarchical clustering (HC) or k-means (KM) for grouping products, attributes, or consumers. However, the results produced by these approaches can differ widely depending on the specific methods used or the initial “seed” aka “starting cluster centroid” chosen in clustering. Although recommendations for various clustering techniques have been made, the realities are that objects in groups can, and do, change their clusters. That can impact interpretation of the data. Researchers usually does not run the clustering algorithms multiple times to determine stability, nor do they often run multiple methods of clustering although that has been recommended previously. This study applied hierarchical agglomerative clustering (HAC), KM and fuzzy clustering (FC) to a large descriptive sensory data set and compared attribute clusters from the methods, including multiple iterations of same methods. Sensory attributes (objects) shuffled among clusters in varying ways, which could provide different interpretations of the data. That frequency was captured in the KM output and used to form the “best possible” clusters via manual clustering (MC). The HAC and FC results were studied and compared with KM results. Attribute correlation coefficients also were compared with clustering information. Using results from one clustering approach may not be reliable, and results should be confirmed using other clustering approaches. A strategy that combines multiple clustering approaches, including a MC process is suggested to determine consistent clusters in sensory data sets.

Wetland vegetation composition and ecology of Lake Abaya in southern Ethiopia.

Wetland vegetation and ecology of Lake Abaya in the southern Ethiopia was studied to determine floristic composition, plant community type and vegetation ecology. A total of 102 plots were laid along transects that were set up preferentially across areas where there were rapid changes in vegetation or marked environmental gradients to collect data on estimate of percentage aerial cover of plant species and environmental variables. Vegetation data was analyzed by agglomerative hierarchical cluster analysis using similarity ratio as a resemblance index and Ward's linkage method. Multivariate data analysis was performed using appropriate packages in R version 2.14.0. Canonical Correspondence Analysis (CCA) was used to explore the relationship between the species composition and environmental variables. The environmental data included in the CCA were determined using stepwise backward and forward selection of variables by ANOVA test. Statistical measurement regarding species diversity, richness and evenness of the plant community types was carried out by using Shannon-Wiener diversity indices. A total of 92 plant species belonging to 66 genera and 34 families were identified. Families Poaceae, Asteraceae, Fabaceae, Cyperaceae, Solanaceae, Euphorbiaceae and Amaranthaceae account for about 56.99% of the total proportion. Based on the cluster analysis, five plant community types were identified. The most important factors influencing the plant species composition and pattern of wetland plant communities were water drainage, water depth, land use, slope, altitude, and hydrogeomorphology. Therefore, these factors should be considered in future management and protection under the circumstance of climate change and human activities.

Changes in wetland landscape and inundation patterns in the middle and lower reaches of the Yangtze River Basin from 1990 to 2020

Wetlands are crucial to diverse societies and ecosystems but are threatened globally. Natural wetlands in the middle and lower reaches of the Yangtze River Basin (MLRYRB) have long been exploited to meet the increasing food and energy demands, especially in recent decades. However, how their landscapes and hydrological patterns changed over time was poorly qualified, which was crucial to conserving biodiversity and ecosystems. In this study, we proposed a new wetland classification procedure taking advantage of the shape, topography, landscape patterns, and phenology to map the distribution of six wetland types in the MLRYRB in 1990 and 2020 with Landsat images. The transitions among different wetland types and associated driving factors were examined. Artificial wetlands expanded by 3907.6 km2 while natural wetlands decreased by 423.2 km2, resulting in a net area increase of 3484.4 km2. Farmland reclamations, aquacultural activities, urban expansion, and dam constructions induced 70 % of the area's loss of natural wetlands. Meanwhile, spatiotemporal patterns in wetland inundation dynamics were determined with agglomerative hierarchical cluster analysis. Widespread transitions from perennial water to seasonal wetlands were detected. Notably, 47.8 % of the natural wetlands experienced significant drying trends, and as a result, the mean yearly inundated periods for natural wetlands decreased from 303 days in 1990 to 272 days in 2020. Given the substantial structural alterations, wetland destruction was more severe than the area reflected. Our results suggested that category-based changes in wetland areas, inundation patterns, associated driving forces, and underlying ecological consequences should be comprehensively assessed to conduct scientific conservation and prevent “paper offsets.”

Ordovician paleobiogeography of the Suborder Cheirurina (Trilobita)

The Suborder Cheirurina is a diverse trilobite clade, ranging from the latest Cambrian to the Devonian. The Ordovician paleobiogeography of the group is explored based on the distribution of genera. Major tectonostratigraphic units and Ordovician ages (Tremadocian, Floian, Dapingian, Darriwilian, Sandbian and Katian) were recorded for each genus. Cluster analysis (agglomerative hierarchical clustering) and non-metric multidimensional scaling (NMDS) were performed independently for each Ordovician age. The results characterize the Ordovician distribution of Cheirurina as two main general patterns. First, there was a marked decrease in endemicity through the Ordovician. Second, latitude seems to play a significant role in the distribution of Cheirurina, at both genus and family levels. Pliomerids and encrinurids occur overwhelmingly at low latitudes. The two main exceptions are Placoparia (Pliomeridae) and Dindymene (Encrinuridae), which are distributed in high latitude terranes. Within Cheiruridae, members of Eccoptochilinae are typical of high latitude Gondwana. Conversely, acanthoparyphines, cheirurines, deiphonines, heliomerines and sphaerexochines are mainly distributed in low or intermediate latitudes.

Data-driven subtypes of mixed semantic-logopenic primary progressive aphasia: Linguistic features, biomarker profiles and brain metabolic patterns

Mixed primary progressive aphasia (mPPA) accounts for a substantial proportion of primary progressive aphasia (PPA) cases. However, the lack of a standardised definition of this condition has resulted in misclassification of PPA cases. In this study, we enrolled 55 patients diagnosed with PPA, comprising 12 semantic variant (svPPA), 23 logopenic variant (lvPPA), and 20 mPPA cases with linguistic characteristics consistent with both svPPA and lvPPA (s/lvPPA). All patients underwent language assessments, evaluation of Alzheimer's disease biomarkers (via cerebrospinal fluid analysis or Amyloid-PET), and 18F-FDG-PET brain scans. An agglomerative hierarchical clustering (AHC) analysis based on linguistic characteristics revealed two distinct clusters within the s/lvPPA group: cluster k1 (n = 10) displayed an AD-like biomarker profile, with lower levels of Aβ42 and Aβ42/Aβ40 ratio, along with higher levels of t-tau and p-tau compared to cluster k2 (n = 10). Interestingly, k1 exhibited linguistic features that were similar to those of svPPA. Both clusters exhibited extensive temporoparietal hypometabolism. These findings support the hypothesis that a subgroup of s/lvPPA may represent a clinical manifestation of AD-related PPA.

Comparative Evaluation of Six Agglomerative Hierarchical Clustering Methods With a Robust Example

The agglomerative hierarchical clustering methods are the most popular type of hierarchical clustering used to group objects in clusters based on their similarity. The methods are represented by a bottom-up approach where each object starts in its cluster and pairs of clusters are merged as it moves up the hierarchy. In this paper, we present six agglomerative hierarchical clustering methods namely: the single linkage method, complete linkage method, average linkage method, centroid method, median method, and Ward’s method. We also evaluated how these methods work on a practical basis using a matrix of distance pairs of five points. It was observed that the single linkage method through its dendrogram produced the most similarity measure between x_i and x_j, while Ward’s method produced the highest distance measure between x_i and x_j.