Cluster Tree Research Articles

A Stepwise‐Clustered Precipitation Downscaling Method for Ensemble Climatic Projections in the Mediterranean Region

ABSTRACTPrecipitation changes dynamically in the Mediterranean region. Therefore, the projection of future precipitation and its historical distribution mechanism is essential for climate mitigation and adaptation. In this study, a stepwise clustered precipitation downscaling method (SCPD) was developed and adopted in the Mediterranean region to reveal the inherent variation rules and trends over the future 100 years under two SSP scenarios. A cutting and merging multivariate process is introduced to build a cluster tree for supporting further downscaling and projecting steps. The ensemble average from the global climate model (GCM) dataset is used for precipitation projections. The precipitation performance of SCPD, evaluated by R2, is fairly decent. The precipitation projections vary with the original rainfall patterns over the gauge stations. Dry places tend to become comparably drier in the future. Precipitation in the northern Mediterranean region shows a drier winter–spring and wetter summer–autumn. Opposite trends emerged in the southern part, with increasing winter precipitation and decreasing summer rainfall. The rising carbon dioxide concentration will further intensify the decrease in rainfall. However, the centres of these two EOFs are not identical. The contributions of NAO (positive) and Niño 3.4 (negative) to PC1 are relatively high. Accordingly, the strongest positive correlation with PC2 is SCAND, as well as negative correlations with AO, NAO and EAWR. Positive anomaly precipitation is attributed to PC1, whereas PC2 is responsible for most of the negative variance precipitation.

Large-scale gene expression data clustering through incremental ensemble approach

Abstract DNA microarray technology monitors gene activity in real-time in living organisms. It creates a large amount of data that helps scientists learn about how genes work. Clustering this data helps understand gene interactions and uncover important biological processes. However, the traditional clustering techniques have difficulties due to the enormous dimensionality of gene expression data and the intricacy of biological networks. Although ensemble clustering is a viable strategy, such high-dimensional data may not lend itself well to traditional approaches. This study introduces a novel technique for gene expression data clustering called incremental ensemble clustering for gene expression data (IECG). There are two steps in the IECG. A technique for grouping gene expression data into windows is presented in the first step, producing a tree of clusters. This procedure is carried out again for succeeding windows that have distinct feature sets. The base clusterings of two consecutive windows are ensembled using a new goal function to form a new clustering solution. By repeating this step-by-step method for further windows, reliable patterns that are beneficial for medical applications can be extracted. The results from both biological and non-biological data demonstrate that the proposed algorithm outperformed the state-of-the-art algorithms. Additionally, the running time of the proposed algorithm has been examined.

Employing cluster analysis in defining groundwater wells patterns in Rafah

Clustering analysis techniques used in identifying homogeneity of groundwater wells patterns in terms of Chloride, Nitrate, and TDS. Data was collected through the laboratories of Palestinian Water Authority in the Rafah 2020. R-Programming is used for data analysis. Kolmogorov-Smirnov test is used to test data normality. Hierarchical clustering analysis applied to generate a cluster tree (r>0.75) in correlation matrix. Agglomerative Hierarchical Clustering is used to classify with “average” method and found that there are two clusters. First cluster with 28 wells and Second cluster with 9 wells. Test of homogeneity between the two clusters groups using T-test for the hierarchical clustering and found that there are significant differences in means for TDS and for Chloride between first cluster and second cluster with a significant level less than 0.01. While for Nitrates, also, there are significant differences in means between the first cluster and second cluster with a significant level less than 0.05. Different stability validation measures carried out for data concludes that for the APN and ADM measures, hierarchical clustering with two clusters again gives the best score. Three internal cluster validation measures are used. Concludes that Hierarchical clustering with two clusters performs the best in (Connective, Dunn, and Silhouette). Recall that the connectivity should be minimized, while both the Dunn Index and the Silhouette Width should be maximized. Thus, Hierarchical clustering outperforms the other clustering algorithms under each validation measure.

Wound Healing Activity of Hydroalcoholic Extracts of Lycopersicon Esculentum Seeds and Ficus Racemosa on Wistar Rats

In the Present study Investigation on the preliminary phytochemicals screening of ethanolic extract showed the presence of Resin, Protin, Quinones, Anthraquinon, Carbohydrates, Alkaloids, Glycoside, Steroids, and Phenols. By ethanol extract using standardized procedure and also subjected to wound healing activity exhibited significant wound healing activity with respect to control biological effects of its extracts ointment, mainly the ones related to wound process. The present data clearly showed that the extract of dried Seeds of Lycoperscon esculentum seeds and Ficus racemosa has wound healing activity the highly significant response of some extracts .The result obtained from the excision wound model, Effect of EECG on Percentage Wound Contraction and Epithelialization Period. During the course of treatment the extract was found to show its preliminary effect from day 4 up to day 16.

Batch effects correction in scRNA-seq based on biological-noise decoupling autoencoder and central-cross loss

Technical or biologically irrelevant differences caused by different experiments, times, or sequencing platforms can generate batch effects that mask the true biological information. Therefore, batch effects are typically removed when analyzing single-cell RNA sequencing (scRNA-seq) datasets for downstream tasks. Existing batch correction methods usually mitigate batch effects by reducing the data from different batches to a lower dimensional space before clustering, potentially leading to the loss of rare cell types. To address this problem, we introduce a novel single-cell data batch effect correction model using Biological-noise Decoupling Autoencoder (BDA) and Central-cross Loss termed BDACL. The model initially reconstructs raw data using an auto-encoder and conducts preliminary clustering. We then construct a similarity matrix and a hierarchical clustering tree to delineate relationships within and between different batches. Finally, we introduce a Central-cross Loss (CL). This loss leverages cross-entropy loss to prompt the model to better distinguish between different cluster labels. Additionally, it employs the Central Loss to encourage samples to form more compact clusters in the embedding space, thereby enhancing the consistency and interpretability of clustering results to mitigate differences between different batches. The primary innovation of this model lies in reconstructing data with an auto-encoder and gradually merging smaller clusters into larger ones using a hierarchical clustering tree. By using reallocated cluster labels as training labels and employing the Central-cross Loss, the model effectively eliminates batch effects in an unsupervised manner. Compared to current methods, BDACL can mitigate batch effects without losing rare cell types.

Characterizing cognitive phenotypes and clinical correlates in type 2 diabetes using fuzzy clustering and decision tree analysis

Cognitive impairment is frequently seen in patients with type 2 diabetes (T2DM), ranging from mild impairment to dementia. However, our knowledge of the specific profiles and risk factors for these different levels of impairment is limited. In this study involving 152 patients with T2DM, cognitive function was assessed using the Montreal Cognitive Assessment test. The Fuzzy C-means clustering algorithm was utilized to group individuals with similar cognitive characteristics. The study evaluated how well clinical parameters could classify characteristics of clusters using the Classification and Regression Trees algorithm. ROC analysis was then used to assess the classification success. Three distinct cognitive clusters were identified. Cluster 1 had the poorest cognitive performance and was characterized by more women, lower education levels, and lower levels of iron, hemoglobin, and creatine. Cluster 3, the amnestic cluster, was distinguished by low TSH levels. The decision tree model highlighted several parameters, including education level, hemoglobin, duration of diabetes mellitus (DM), iron, TSH, gender, family history of diabetes, and microalbumin/creatinine ratio, as significantly affecting the distinction of cognitive clusters. Diabetes-associated cognitive impairment stems from multifaceted pathophysiological mechanisms influenced by complex risk factors, resulting in diverse types of cognitive deficits.

Comparative analysis of the whole mitochondrial genomes of four species in sect. Chrysantha (Camellia L.), endemic taxa in China

BackgroundThe sect. Chrysantha Chang of plants with yellow flowers of Camellia species as the “Queen of the Tea Family”, most of these species are narrowly distributed endemics of China and are currently listed Grde-II in National Key Protected Wild Plant of China. They are commercially important plants with horticultural medicinal and scientific research value. However, the study of the sect. Chrysantha species genetics are still in its infancy, to date, the mitochondrial genome in sect. Chrysantha has been still unexplored.ResultsIn this study, we provide a comprehensive assembly and annotation of the mitochondrial genomes for four species within the sect. Chrysantha. The results showed that the mitochondrial genomes were composed of closed-loop DNA molecules with sizes ranging from 850,836 bp (C. nitidissima) to 1,098,121 bp (C. tianeensis) with GC content of 45.71–45.78% and contained 48–58 genes, including 28–37 protein-coding genes, 17–20 tRNA genes and 2 rRNA genes. We also examined codon usage, sequence repeats, RNA editing and selective pressure in the four species. Then, we performed a comprehensive comparison of their basic structures, GC contents, codon preferences, repetitive sequences, RNA editing sites, Ka/Ks ratios, haplotypes, and RNA editing sites. The results showed that these plants differ little in gene type and number. C. nitidissima has the greatest variety of genes, while C. tianeensis has the greatest loss of genes. The Ka/Ks values of the atp6 gene in all four plants were greater than 1, indicating positive selection. And the codons ending in A and T were highly used. In addition, the RNA editing sites differed greatly in number, type, location, and efficiency. Twelve, six, five, and twelve horizontal gene transfer (HGT) fragments were found in C. tianeensis, Camellia huana, Camellia liberofilamenta, and C. nitidissima, respectively. The phylogenetic tree clusters the four species of sect. Chrysantha plants into one group, and C. huana and C. liberofilamenta have closer affinities.ConclusionsIn this study, the mitochondrial genomes of four sect. Chrysantha plants were assembled and annotated, and these results contribute to the development of new genetic markers, DNA barcode databases, genetic improvement and breeding, and provide important references for scientific research, population genetics, and kinship identification of sect. Chrysantha plants.

TCα-PIA: A Personalized Social Network Anonymity Scheme via Tree Clustering and α-Partial Isomorphism

Social networks have become integral to daily life, allowing users to connect and share information. The efficient analysis of social networks benefits fields such as epidemiology, information dissemination, marketing, and sentiment analysis. However, the direct publishing of social networks is vulnerable to privacy attacks such as typical 1-neighborhood attacks. This attack can infer the sensitive information of private users using users’ relationships and identities. To defend against these attacks, the k-anonymity scheme is a widely used method for protecting user privacy by ensuring that each user is indistinguishable from at least k−1 other users. However, this approach requires extensive modifications that compromise the utility of the anonymized graph. In addition, it applies uniform privacy protection, ignoring users’ different privacy preferences. To address the above challenges, this paper proposes an anonymity scheme called TCα-PIA (Tree Clustering and α-Partial Isomorphism Anonymization). Specifically, TCα-PIA first constructs a similarity tree to capture subgraph feature information at different levels using a novel clustering method. Then, it extracts the different privacy requirements of each user based on the node cluster. Using the privacy requirements, it employs an α-partial isomorphism-based graph structure anonymization method to achieve personalized privacy requirements for each user. Extensive experiments on four public datasets show that TCα-PIA outperforms other alternatives in balancing graph privacy and utility.

Assessment of the Possibility of Using the Trace Element Status of a Herbal Drug as an Additional Quality Criterion: Chamomile Flowers as a Case Study

INTRODUCTION. Herbal drugs can often be difficult to identify and distinguish from morphologically similar species, especially when fractionated. For the analysis of complex mixtures of herbal origin, it is reasonable to use additional identification methods, including hierarchical clustering of herbal drugs by their trace element content.AIM. This study aimed to assess the possibility of using hierarchical clustering by trace element content for complex biological matrices (plant mixtures), with officinal chamomile species as a case study.MATERIALS AND METHODS. Multiple morphologically similar species grow together with officinal chamomile species that are widely used in medical practice. The study examined the flowers and peduncles of Matricaria recutita L., Tanacetum parthenium (L.) Sch.Bip., Leucanthemum vulgare Lam., Tripleurospermum inodorum (L.) Sch.Bip., and Matricaria suaveolens Buchenau, as well as artificial mixtures of chamomile with adulterant species (as samples non-compliant with pharmacopoeial specifi ca tions). The content of trace elements was determined by inductively coupled plasma mass spectrometry (ICP-MS). The statistical treatment of the results used Statistica 10 software with a built-in data analysis algorithm for multivariate experiments.RESULTS. Having determined the content of 56 trace elements, the authors plotted a hierarchical cluster tree using cluster analysis methods. M. recutita and M. suaveolens were shown to be the closest to each other in terms of their trace element status, which correlated with their taxonomy. Artificial plant mixtures formed intermediate clusters that were observed between different chamomile species. Moreover, according to the criterion of dissimilarity, the trace element cluster of M. recutita significantly differed from the trace element clusters of all the adulterant species and mixtures containing more than 10% of the adulterant species.CONCLUSIONS. This study demonstrated the possibility of using hierarchical clustering based on the content of trace elements in a biological object to analyse complex systems, including plant mixtures, and identify differences that may have discriminative value for the quality control of herbal drugs.

Evaluation of Protective effects of Fermented Ficus racemosa Fruit Extract against Oxidative Stress and Hyperglycemia

Evaluation of Protective effects of Fermented Ficus racemosa Fruit Extract against Oxidative Stress and Hyperglycemia

Enhancing Black Hole Attack Detection in VANETs: A Hybrid Approach Integrating DBSCAN Clustering with Decision Trees

Ensuring the security of communication is crucial in Vehicular Ad Hoc Networks (VANETs) to protect the integrity of information sharing among cars. To implement VANET communication as an answer for the different uses, secure communication is necessary. The unreliability of VANET environments is caused by message delays or tampering in VANET applications. Finding the sweet spot between VANET security and performance and dependability is the primary goal. This project’s overarching goal is to fortify VANETs against Blackhole Routing Attacks and, by identifying and blocking harmful nodes, to mitigate the blackhole impact. This paper proposes a robust hybrid approach for the detection of black hole attacks in VANETs, leveraging the synergy between DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Decision Trees. DBSCAN, a density-based clustering algorithm, is employed to identify spatial clusters of vehicles, while Decision Trees are utilized to discern normal communication patterns from malicious ones within these clusters. The integration of these two techniques enhances the accuracy and efficiency of black hole attack detection in the dynamic and resource-constrained VANET environment. Experimental results demonstrate the effectiveness of the proposed hybrid approach, providing a promising solution for bolstering the security of VANETs against emerging threats. Here in result 73.89% improvement is received in Packet Drop Rate using DBSCAN, also minor improvement over Throughput and Average end to end delay and major improvement in terms of Network Routing Load.

Utilizing echocardiography and unsupervised machine learning for heart failure risk identification

BackgroundGlobal longitudinal strain (GLS) is recognized as a powerful predictor of heart failure (HF). However, the entire strain curve may entail important prognostic information regarding HF risk that might be undiscovered by only focusing on the peak strain value. ObjectiveThe hypothesis of the present study was, that analysis of the entire strain curve using unsupervised machine learning (uML) would reveal novel ventricular deformation patterns capable of predicting incident HF independently of GLS. MethodsLongitudinal strain curves from 3710 subjects from the general population without prevalent HF were analyzed using uML. ResultsMean age was 56 years and 43 % were male. During a median follow-up of 5.3 years, 92 subjects (2.5 %) developed HF. The uML algorithm generated a hierarchical clustering tree (HCT) resulting in 10 different clusters. Generally, the strain curves displayed reduced early diastolic strain to peak-strain ratio with an increasing incidence rate of HF. In multivariable Cox regressions, cluster 9 was significantly associated with increased risk of HF when compared to cluster 2–5, and 7–8 [For cluster 3: HR 8.95, 95 %CI: 2.08;38.48, P = 0.003] even though the subjects of cluster 9 were younger, displayed healthier clinical baseline characteristics, and only had slightly reduced GLS. The mean strain curve of cluster 9 displayed an early systolic lengthening followed by a late and reduced contraction specifically related to the basal lateral segment. ConclusionThe unsupervised machine learning algorithm identified unknown strain patterns beyond GLS presumably related to increased risk of HF.

Phytochemical screening of therapeutic antidiabetic preparation of cluster fig curry and bitter gourd leaves salad

Phytochemical screening of therapeutic antidiabetic preparation of cluster fig curry and bitter gourd leaves salad

Two new plant records, Alangium kurzii (Cornaceae) and Ficus racemosa (Moraceae), in Bhutan and corresponding distributions

Bhutan represents a biodiversity hotspot in the eastern Himalayan region, known for its unique environment and diverse plant species. This has made Bhutan a popular destination for plant enthusiasts from all over the world. Several new plant discoveries and records have been made in recent years, contributing valuable additions to scientific knowledge and the country’s botanical records. Here, we provide two new additional records of plants for the flora of Bhutan: Alangium kurzii Craib from the Cornaceae family and Ficus racemosa L. from the Moraceae family. With these additions, Bhutan now has three Alangium species and 25 Ficus species including the current records. We also present their uses and distributions in the country.

Green synthesis of copper oxide nanoparticles using Ficus racemosa leaf extract and investigation of its antibacterial properties

ABSTRACT The present work reports a one-pot green synthesis of copper oxide nanoparticles (CuO NPs) using Ficus racemosa leaf extract via a green chemistry approach without using any harsh chemical reagents. The crystalline and single-phase CuO NPs were analysed via XRD. DLS revealed the average particle size distribution, and zeta potential was used for the evaluation of surface charge. Optical properties were determined at 530 nm through UV–Vis spectroscopy with a broad peak in the range from 500 to 590 nm. Different functional groups in the leaf extract containing flavonoids and polyphenols as reducing agents responsible for the synthesis of CuO NPs were verified through FTIR. The FESEM images revealed the average particle size in the range from 30 to 300 nm with a spherical morphology, and EDS was used for the quantitative elemental analysis. The strong bacterial inhibition tendency of CuO NPs against 12 bacterial strains including Bacillus cereus, Campylobacter jejuni, Bacillus subtilis, Clostridium perfringens, Staphylococcus aureus, Escherichia coli, Streptococcus pyogenes, Vibrio cholerae, Shigella dysenteriae, Listeria monocytogenes, Helicobacter pylori, and Salmonella typhi was observed with MIC values between 60 and 1000 µg/mL. The findings suggest that the synthesis of CuO NPs via green chemistry can serve as a potential substitute for traditional multi-step chemical synthetic routes.

Molecular cluster, transmission characteristics, origin and dynamics analysis of HIV-1 CRF59_01B in China: A molecular epidemiology study

PurposeThis study investigated for the HIV-1 CRF59_01B epidemic's spatiotemporal dynamics and its transmission networks in China. MethodsBetween 2007 and 2020, a total of 250 partial pol gene sequences of HIV-1 CRF59_01B were collected from four regions (10 Chinese provinces). Phylogenetic tree construction and cluster identification were then performed. The Bayesian skyline and birth–death susceptible–infected–removed models were employed for the phylodynamic analyses of subtypes and large clusters, respectively. Phylogenetic analyses and trait diffusion of these sequences were performed using Bayesian phylogenetic methods (beast-classic package). Distance-based molecular network analyses were performed to identify putative relationships. ResultsUsing a genetic distance threshold of 1.3 %, We identified 45 clusters that included 62.40 % (156/250) of the sequences. Three clusters (6.67 %, 3/45) had 10 or more sequences, and were considered "large clusters". Six clusters (13.33 %) included sequences from different regions (Southeast, Northeast, Southeast, and Central China). Thirteen clusters (28.89 %) included sequences of men who had sex with men only, three clusters (6.67 %) included sequences of heterosexuals only, and 12 clusters (26.67 %) included sequences of both groups. The substitution rate of CRF59_01B was 1.91 × 10–3 substitutions per site per year [95 % highest posterior density (HPD) interval: 1.39 × 10−3–2.49 × 10−3)], the time to the most recent common ancestor of CRF59_01B was to be 1992.83 (95 % HPD: 1977.97–2002.81). A Bayesian skyline plot revealed that the effective population size of CRF59_01B increased from 2000 to 2015 and remained stable after 2015. The large clusters showed continuous growth from 2013 to 2020. Phylogeographic analysis showed that CRF59_01B B most likely originated in Southeast China, with a posterior probability of 97.44 %, and then spread to other regions. ConclusionsOur study revealed the temporal and geographical origins of HIV-1 CRF59_01B as well as the process of transmission among various regions and risk groups in China, which can help develop targeted HIV prevention strategies.

Interpretable sequence clustering

Categorical sequence clustering is vital across various domains; however, the interpretability of cluster assignments presents considerable challenges. Sequences inherently lack explicit features, and existing sequence clustering algorithms heavily rely on complex representations, which complicates the explanation of their outcomes. To address this issue, we propose a method called Interpretable Sequence Clustering Tree (ISCT), which combines sequential patterns with a concise and interpretable tree structure. ISCT leverages k−1 patterns to generate k leaf nodes, corresponding to k clusters, which provides an intuitive explanation on how each cluster is formed. More precisely, ISCT first projects sequences into random subspaces and then utilizes the k-means algorithm to obtain high-quality initial cluster assignments. Subsequently, it constructs a pattern-based decision tree using a boosting strategy in which sequences are re-projected and re-clustered at each node before mining the top-1 discriminative splitting pattern. Experimental results on 14 real-world data sets demonstrate that our proposed method provides an interpretable tree structure while delivering fast and accurate cluster assignments.

Decision Tree Clusters: Non-destructive detection of overheating defects in porcelain insulators using quantitative thermal imaging techniques

Porcelain insulators are subject to performance deterioration during operation under the influence of complex environments, thus increasing the occurrence of flashover accidents. Regular inspection of insulator condition is of great significance for the stable operation of power grids. Therefore, we propose a non-destructive detection method of insulator overheating defects using quantitative thermography. It can be used for defect detection of porcelain insulators with internal overheating defects. In this study, the relationship between the insulating properties of porcelain insulators and the heating anomalies was first analyzed. Then, Decision Tree Clusters (DTC) algorithm is used to extract the spatial temperature feature information to achieve the defective insulator location capture. Finally, Submodular-pick Local Interpretable Model-agnostic Explanations (SLIME) is used for model decision visualization to verify the feasibility of the technique. The experimental results show that DTC not only ensures the accuracy of insulator long-distance detection, but also realizes the quantitative detection of insulators.

Comparative analysis of codon usage bias in the chloroplast genomes of eighteen Ampelopsideae species (Vitaceae)

BackgroundThe tribe Ampelopsideae plants are important garden plants with both medicinal and ornamental values. The study of codon usage bias (CUB) facilitates a deeper comprehension of the molecular genetic evolution of species and their adaptive strategies. The joint analysis of CUB in chloroplast genomes (cpDNA) offers valuable insights for in-depth research on molecular genetic evolution, biological resource conservation, and elite breeding within this plant family.ResultsThe base composition and codon usage preferences of the eighteen chloroplast genomes were highly similar, with the GC content of bases at all positions of their codons being less than 50%. This indicates that they preferred A/T bases. Their effective codon numbers were all in the range of 35–61, which indicates that the codon preferences of the chloroplast genomes of the 18 Ampelopsideae plants were relatively weak. A series of analyses indicated that the codon preference of the chloroplast genomes of the 18 Ampelopsideae plants was influenced by a combination of multiple factors, with natural selection being the primary influence. The clustering tree generated based on the relative usage of synonymous codons is consistent with some of the results obtained from the phylogenetic tree of chloroplast genomes, which indicates that the clustering tree based on the relative usage of synonymous codons can be an important supplement to the results of the sequence-based phylogenetic analysis. Eventually, 10 shared best codons were screened on the basis of the chloroplast genomes of 18 species.ConclusionThe codon preferences of the chloroplast genome in Ampelopsideae plants are relatively weak and are primarily influenced by natural selection. The codon composition of the chloroplast genomes of the eighteen Ampelopsideae plants and their usage preferences were sufficiently similar to demonstrate that the chloroplast genomes of Ampelopsideae plants are highly conserved. This study provides a scientific basis for the genetic evolution of chloroplast genes in Ampelopsideae species and their suitable strategies.

Effect of Azadirachta Indica Tree Clusters on Health of Trees and Analysis with Agroforestry Developed for Livestock Grazing and Sustainability

Effect of Azadirachta Indica Tree Clusters on Health of Trees and Analysis with Agroforestry Developed for Livestock Grazing and Sustainability