Component Cluster Research Articles

Application of Multidimensional Data Analysis in Network Intrusion Detection System

It is important to understand the behavior of an information network and its features. In this research, we explore this idea by applying a multidimensional data analysis system, a system that is significant in enhancing data intrusion detection. To accomplish this, we gather data from various sources, like network traffic, user behavior, and system logs. Using the examples mentioned, the proposed system detects and prevents cyber threats more accurately. Artificial Intelligence techniques including deep learning, clustering, and principal component analysis (PCA), are used. These are essential in analyzing complex patterns within the data and enabling the detection of sophisticated and evolving intrusion techniques. Multidimensional data allows for the capture of intricate, non-linear relationships. This improves the system’s ability to differentiate between normal and abnormal activities. Real-time data processing and AI-driven algorithms enhance detection speed and enable faster responses to potential intrusions. We tested this system on benchmark datasets. Results showed significant improvements in detection rates and a reduction in false positives compared to traditional NIDS. The integration of AI gave us a more adaptive and scalable approach to intrusion detection. Also, it allowed the system to learn from new attack patterns and continuously refine its capabilities.

A conceptual analysis of verbs of pushing and pulling

Although verbal expressions of caused motion, such as push and pull, have been extensively studied within linguistics, semantic dimensions beyond path and manner of motion have received less attention. This pilot study aims to identify such dimensions involved in the expression of caused motion in German, focusing on observable properties of pushing and pulling events that determine the selection of verbs to describe events of caused motion. Using 3D graphical modeling, participants were presented with video clips of a computer-animated agent moving a barrel, thereby allowing for a systematic manipulation of properties and hence dimensions. We investigated four dimensions to assess their impact on verb selection: (i) angle of contact, (ii) movement of the agent relative to the barrel, (iii) the agent’s orientation/facing, and (iv) the force employed. Cluster and principal component analyses were conducted on the collected linguistic data. Verbs were represented by five-dimensional vectors capturing correlations with the cosine and sine of the angle, and marginal probabilities in conditions of instantaneous movement, forward facing, and heavy force. Our findings indicate that conceptually distinguishable verb clusters are primarily defined by the movement feature – that is, whether the agent moves together with the barrel or not – and the cosine of the angle. Contrary to theoretical predictions, little evidence was found supporting the categorization of verbs based on the force applied to the barrel. These results suggest that the movement and position of the agent relative to the moved object are key determinants in the production of verbal descriptions of caused motion events.

Genetic Tendency Analysis and Comprehensive Antioxidant Activity Evaluation of Leaves and Flowers of Loquat F1 Generation.

Loquat leaves, flowers, and other organs contain abundant antioxidant substances, which have wide applications in medicine, health, and food industries. This study aims to provide theoretical guidance for loquat hybrid parent and combination selection and a basis for high-quality loquat strain screening and development. For comprehensive antioxidant profiling, we used "Ninghaibai" and "Oobusa" loquat and their F1 generation as experimental materials to determine the total phenol, flavonoid, DPPH, ABTS, and FRAP content in the leaves and flowers of 56 strains. Five traits, including total phenols, flavonoids, DPPH, ABTS, and FRAP, were widely separated and normally distributed in the flowers of 56 F1 loquat strains, exhibiting the genetic basis of these quantitative traits. However, these traits displayed widely separated and slightly skewed distribution in the leaves of the F1 generation. The total phenols, flavonoids, DPPH, and FRAP showed a trend of small inheritance in the leaves. However, the ABTS showed a trend of medium and high inheritance in leaves and flowers, respectively. Through cluster and principal component analyses, a comprehensive antioxidant activity evaluation was conducted. Ten strains with comprehensive scores greater than 1 for antioxidant activity in leaves and flowers were selected. Among them, the top three strains with high antioxidant capacity were ND107, "Oobusa", and ND128. These results suggest that hybrid breeding guided by the genetic characteristics of each trait can improve the possibility of cultivating new varieties with high antioxidant activity.

Six-Dimensional Pose Estimation of Molecular Sieve Drying Package Based on Red Green Blue-Depth Camera.

This paper aims to address the challenge of precise robotic grasping of molecular sieve drying bags during automated packaging by proposing a six-dimensional (6D) pose estimation method based on an red green blue-depth (RGB-D) camera. The method consists of three components: point cloud pre-segmentation, target extraction, and pose estimation. A minimum bounding box-based pre-segmentation method was designed to minimize the impact of packaging wrinkles and skirt curling. Orientation filtering combined with Euclidean clustering and Principal Component Analysis (PCA)-based iterative segmentation was employed to accurately extract the target body. Lastly, a multi-target feature fusion method was applied for pose estimation to compute an accurate grasping pose. To validate the effectiveness of the proposed method, 102 sets of experiments were conducted and compared with classical methods such as Fast Point Feature Histograms (FPFH) and Point Pair Features (PPF). The results showed that the proposed method achieved a recognition rate of 99.02%, processing time of 2 s, pose error rate of 1.31%, and spatial position error of 3.278 mm, significantly outperforming the comparative methods. These findings demonstrated the effectiveness of the method in addressing the issue of accurate 6D pose estimation of molecular sieve drying bags, with potential for future applications to other complex-shaped objects.

Challenges in blood fractionation for cancer liquid biopsy: how can microfluidics assist?

Liquid biopsy provides a minimally invasive approach to characterise the molecular and phenotypic characteristics of a patient's individual tumour by detecting evidence of cancerous change in readily available body fluids, usually the blood. When applied at multiple points during the disease journey, it can be used to monitor a patient's response to treatment and to personalise clinical management based on changes in disease burden and molecular findings. Traditional liquid biopsy approaches such as quantitative PCR, have tended to look at only a few biomarkers, and are aimed at early detection of disease or disease relapse using predefined markers. With advances in the next generation sequencing (NGS) and single-cell genomics, simultaneous analysis of both circulating tumour DNA (ctDNA) and circulating tumour cells (CTCs) is now a real possibility. To realise this, however, we need to overcome issues with current blood collection and fractionation processes. These include overcoming the need to add a preservative to the collection tube or the need to rapidly send blood tubes to a centralised processing lab with the infrastructure required to fractionate and process the blood samples. This review focuses on outlining the current state of liquid biopsy and how microfluidic blood fractionation tools can be used in cancer liquid biopsy. We describe microfluidic devices that can separate plasma for ctDNA analysis, and devices that are important in isolating the cellular component(s) in liquid biopsy, i.e., individual CTCs and CTC clusters. To facilitate a better understanding of these devices, we propose a new categorisation system based on how these devices operate. The three categories being 1) solid Interaction devices, 2) fluid Interaction devices and 3) external force/active devices. Finally, we conclude that whilst some assays and some cancers are well suited to current microfluidic techniques, new tools are necessary to support broader, clinically relevant multiomic workflows in cancer liquid biopsy.

Identifying Candidate Genes Related to Soybean (Glycine max) Seed Coat Color via RNA-Seq and Coexpression Network Analysis.

The quality of soybeans is reflected in the seed coat color, which indicates soybean quality and commercial value. Researchers have identified genes related to seed coat color in various plants. However, research on the regulation of genes related to seed coat color in soybeans is rare. In this study, four lines of seed coats with different colors (medium yellow 14, black, green, and brown) were selected from the F2:5 population, with Beinong 108 as the female parent and green bean as the male parent, and the dynamic changes in the anthocyanins in the seed coat were stained with 4-dimethylaminocinnamaldehyde (DMACA) during the grain maturation process (20 days from grain drum to seed harvest). Through RNA-seq of soybean lines with four different colored seed coats at 30 and 50 days after seeding, we can further understand the key pathways and gene regulation modules between soybean seed coats of different colors. DMACA revealed that black seed coat soybeans produce anthocyanins first and have the deepest staining. Clustering and principal component analysis (PCA) of the RNA-seq data divided the eight samples into two groups, resulting in 16,456 DEGs, including 5359 TFs. GO and KEGG enrichment analyses revealed that the flavonoid biosynthesis, starch and sucrose metabolism, carotenoid biosynthesis, and circadian rhythm pathways were significantly enriched. We also conducted statistical and expression pattern analyses on the differentially expressed transcription factors. Based on weighted gene coexpression network analysis (WGCNA), we identified seven specific modules that were significantly related to the four soybean lines with different seed coat colors. The connectivity and functional annotation of genes within the modules were calculated, and 21 candidate genes related to soybean seed coat color were identified, including six transcription factor (TF) genes and three flavonoid pathway genes. These findings provide a theoretical basis for an in-depth understanding of the molecular mechanisms underlying differences in soybean seed coat color and provide new genetic resources.

Princilpal cluster component analysis revisited

Princilpal cluster component analysis revisited

Efficient Image Retrieval in Fashion: Leveraging Clustering and Principal Component Analysis for Search Space Reduction

In this study, a novel approach using clustering techniques and Principal Component Analysis (PCA) for reducing the search space in fashion image retrieval systems is introduced. The study focuses on extracting high-dimensional feature vectors from images of clothing items and finding the same or the most similar product using these feature vectors, thereby narrowing the search space. The proposed method employs unsupervised learning algorithms to analyze high-dimensional fashion image feature vectors, grouping them into meaningful clusters. This enhances search efficiency and improves user experience. By reducing the dimensionality of feature vectors with PCA, computational costs are minimized. Experimental results demonstrate that the proposed method significantly accelerates computation time while maintaining an acceptable level of accuracy.

Star formation beyond galaxies: widespread in-situ formation of intra-cluster stars

We study the fraction of the intra-cluster light (ICL) formed in-situ in the three most massive clusters of the TNG50 simulation, with virial masses ∼1014. We find that a significant fraction of ICL stars ( 8%- 28%) are born in-situ. This amounts to a total stellar mass comparable to the central galaxy itself. Contrary to simple expectations, only a sub-dominant fraction of these in-situ ICL stars are born in the central regions and later re-distributed to more energetic orbits during mergers. Instead, many in-situ ICL stars form directly hundreds of kiloparsecs away from the central galaxy, in clouds condensing out of the circum-cluster medium. The simulations predict a present-date diffuse star formation rate of $$1 M⊙/yr, with higher rates at higher redshifts. The diffuse star forming component of the ICL is filamentary in nature, extends for hundreds of kiloparsecs and traces the distribution of neutral gas in the cluster host halo. We discuss briefly how numerical details of the baryonic treatment in the simulation, in particular the density threshold for star formation and the equation of state, may play a role in this result. We conclude that a sensitivity of 1.6×10−19−2.6×10−18 erg s −1 cm −2 arcsec −2 in H α flux (beyond current observational capabilities) would be necessary to detect this diffuse star-forming component in galaxy clusters.

Effect of γ-irradiation combined with enzymatic modification on the physicochemical properties of defatted rice bran dietary fiber

This study comprehensively examines how combining γ-irradiation and enzymatic modification influences the microstructure and physicochemical properties of dietary fiber (DF) obtained from defatted rice bran. The resulting yields of soluble dietary fiber (SDF) and insoluble dietary fiber (IDF) were measured at 13.38 ± 0.40 g/100 g and 52.19 ± 0.97 g/100 g, respectively. The modifications led to a diminish in particle size, an increase in specific surface area, and an improvement in water-holding capacity, oil-holding capacity, swelling capacity, glucose adsorption capacity, and cholesterol adsorption capacity. Furthermore, the modified DF exhibited enhanced anti-digestive properties and probiotic activity. Cluster and principal component analysis results revealed that the modified SDF exhibited superior functional properties. Correlation analysis indicated a noticeable relationship between the monosaccharide composition of DF and its functional characteristics. These findings suggest that γ-irradiation combined with enzymatic modification represents a viable approach for enhancing the quality of rice bran DF.

Identification of key neutrophil extracellular trap genes in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that is associated with neuroinflammation. Neutrophil extracellular traps (NETs) are web-like structures that cause inflammation, but its involvement in AD pathogenesis is unclear. This study aimed to identify key NETs related genes associated with AD. A total of 180 samples from the GSE122063 and GSE36980 dataset were obtained from the GEO repository. The representative genes were obtained, and its diagnostic performance was evaluated by analyzing operating characteristic curves. Consensus clustering and principal component analysis were performed to cluster AD samples. Gene ontology, KEGG pathway enrichment, and protein interaction network were analyzed. Peripheral blood samples were collected from 5 AD patients and 5 healthy donors to determine the expression of representative proteins or genes using WB, ELISA, and RT-qPCR. A total of 297 differentially expressed genes (DEGs) were associated with AD, 18 NETs genes showed potential diagnostic value. NETs genes expression effectively distinguished AD patients into 2 subgroups. The AD1 cluster showed higher abundance of activated dendritic cells, monocytes, and neutrophils, the AD2 cluster showed higher levels of naive B cells, eosinophils, and activated NK cells. We randomly selected and measured the levels representative genes in AD patients. The levels of NETs and CCL2, TLR2 expressions were significantly increased, whereas the level of BDNF was significantly decreased in AD patients comparing with healthy controls. This study identified key NET genes including BDNF, CCL2, and TLR2 to be associated with AD pathogenesis and classification.

Integrative multivariate analysis of agro-chemical and genotypic traits in Indian catmint (Nepeta cataria L.) half-sib populations

This investigation conducted a comprehensive multivariate analysis on thirty half-sib populations of Nepeta cataria L., derived from natural populations based on oil yield. The study addresses the lack of comprehensive data on genetic, phenotypic, and chemical diversity in Nepeta cataria, which is essential for material selection and for conducting effective breeding programs. A substantial amount of phenotypic diversity was observed for all eleven quantitative traits measured in the present study. The phenotypic diversity was assessed using Euclidean distance-based agglomerative hierarchical clustering and principal component analysis (PCA) which showed more than 50 % diversity existing among the studied populations. These methods delineated the populations into three primary clusters, where herb yield was a key discriminator. The GC and GC/MS-based chemical profiling of these populations identified eleven major phytochemicals. Though, significant variances in the concentrations of various nepetalactone isomers were noted, correlating with the phenotypic clusters, the major differentiation was observed for cis-trans Nepetalactone (73.45%–93.20 %). Three distinct chemical clusters were defined, each characterized by unique chemical signatures. Molecular diversity was evaluated using 21 Start Codon Targeted Polymorphic (SCoT) and 20 CAAT box-derived DNA Polymorphic (CBDP) markers, revealing high levels of polymorphism. Populations were again grouped into three clusters. A correlative Mantel test performed to determine the association between agro-chemical and genotypic traits revealed minimal correlation between them, indicating a complex interplay between genetic makeup and phenotypic traits across these populations. This study underscores the genetic and chemical diversity within Nepeta cataria half-sib populations, providing insights that could influence future breeding and conservation efforts. The distinct clustering based on agronomic, chemical, and genotypic data highlights the potential for targeted selection and breeding programs within this species. Future research should focus on integrating these findings into breeding strategies for enhancing commercial cultivation.

Cluster and Principal Component Analyses of the Bioactive Compounds and Antioxidant Activity of Celery (Apium graveolens L.) Under Different Fertilization Schemes.

This research investigates the impact of various fertilization methods on the bioactive compound content and antioxidant activity of celery (Apium graveolens L.) root and leaf. Mineral fertilizer, poultry manure, cattle manure, sheep manure, supercompost, and molasses were applied. Total dry weight, phenolic and flavonoid compounds, and antioxidant activity were assessed, along with fiber, protein, fat, sugar, and starch in celery root. Principal component analysis (PCA) and cluster analysis were used to correlate production conditions with the parameters. The highest fiber and protein contents were found in mineral-fertilized roots, while total fat and sugar were highest in cattle-manure-fertilized roots, and starch was highest in supercompost-fertilized roots. Fertilization with supercompost yielded the highest total phenolic and flavonoid contents in leaves, while mineral fertilizer resulted in the highest antioxidant activity in roots. Notably, the highest dry weight in leaves and the highest total phenolic and flavonoid contents in roots were also observed with supercompost. PCA and cluster analysis demonstrated significant correlations between plant parts, i.e., the celery root and leaf samples, cultivation conditions, and the observed parameters, emphasizing the importance of selecting suitable cultivation methods to optimize celery's nutritional properties. Also, these findings suggest that supercompost, a byproduct of breweries, could potentially replace animal-based organic fertilizers, addressing the problem of reduced availability due to declining livestock numbers.

Retinal blood vessel segmentation using density-based fuzzy C-means clustering and vessel neighborhood connected component

Retinal blood vessel segmentation is a crucial process in medical image analysis. However, it is often challenging due to the variation in color, shape, intensity, size, and contrast of blood vessels. Most of the relevant studies concentrate on algorithms based on supervised learning and few on deep learning. However, due to the several challenges in retinal image acquisition, these algorithms cannot deliver the highest possible level of accuracy. Therefore, this paper implements retinal blood vessel segmentation and classification (RBVSC) methods using density-based fuzzy C-means clustering and vessel neighborhood-connected components, hereafter denoted as DBFCM-VNCC. Initially, the given retinal images are preprocessed using the contrast enhancement method that involves Histogram Equalization with Variable Enhancement Degree (HEVED), selecting the appropriate color channel, optic disc elimination, and using a Gaussian filter, which removes the noise or artifacts from the retinal images. Then, a fully fuzzy C-means clustering is used for coarse segmentation of vessel lesions, which can detect the affected blood vessel features quite efficiently. Finally, structure-based algorithms based on vessel neighborhood-connected components based on mathematical dilation operators and local thicknesses are used to obtain accurate skeletonization and segmentation of the retinal vessels. The algorithm was assessed using three open-access retinal image databases: DRIVE, CHASE_DB1, and HRF, where it achieved mean sensitivity, specificity, accuracy, area overlap measure, and error rate scores of 98.16%, 98.74%, 97.68%, and 4.54%; 98.25%, 98.81%, 97.68%, 97.86%, and 2.14%; 98.22%, 98.78%, 97.56%, 97.40%, and 2.60% for segmenting the retinal vessel. This demonstrates the effectiveness of the proposed DBFCM-VNCC techniques.

Identification of Molecular Subtypes and Prognostic Traits Based on Chromosomal Instability Phenotype-Related Genes in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) exhibits significant molecular heterogeneity; however, previous studies have not fully explored its classification into distinct molecular subtypes. Here, we identified LUAD-significant chromosomal instability (CIN) phenotype genes (n = 24) using a TCGA-LUAD cohort (n = 592) and evaluated their ability to predict pathologic grade. Unsupervised clustering and principal component analysis revealed that LUAD patients could be classified into CIN phenotype-related subtypes (GroupLow, GroupModerate, and GroupHigh), each exhibiting distinct transcriptomic patterns. Notably, the GroupHigh showed significantly poor overall survival [OS; hazard ratio (HR) = 1.43, p-value < 10-3] and disease-free survival (DFS; HR = 1.27, p-value < 10-3). Univariate and multivariate analysis confirmed that its expression status was an independent prognostic predictor (p-value < 10-3, HR = 2.18, 95% C.I = 1.26-3.76) of the clinical outcomes, outperforming pathologic grade (p-value < 10-3, HR = 1.2, 95% C.I = 1.08-1.33). Moreover, analysis of surfactant metabolism-related genes revealed higher expression in the GroupLow, which was associated with a favorable prognosis. By integrating multiple independent cohorts (n = 779), we validated these findings and confirmed that CIN phenotype gene status serves as a critical prognostic marker in LUAD. Furthermore, genomic profiling showed that the GroupHigh exhibited frequent mutations in key genes such as KEAP1, LYST, SETD2, and TP53, with oncogenes in this group preferentially showing copy number gains. Our study highlights the significance of CIN phenotype gene status as a predictor of LUAD prognosis and its association with transcriptomic and genomic alterations, paving the way for further clinical validation and potential therapeutic interventions.

Genomic and Transcriptomic Comparison Between Invasive Non-Typhoidal Salmonella and Non-Invasive Isolates.

Invasive non-typhoidal Salmonella (iNTS) poses a significant threat to global public health. Salmonella enterica Enteritidis and Typhimurium are the primary serovars responsible for both invasive diseases and gastroenteritis. This study aims to investigate the genomic and transcriptomic differences between isolates associated with these contrasting clinical presentations. We retrieved genomes of Salmonella Enteritidis and Salmonella Typhimurium from Enterobase, utilizing blood and stool isolates as representatives for iNTS and non-iNTS, respectively. An indistinguishable phylogenetic relationship was revealed between the blood and stool isolates for both serovars. Few genes were specifically identified in iNTS. Random forest and principal coordinates analysis permitted moderate discrimination between the two sources of isolates based on overall genome content. Notably, the blood isolates of Salmonella Typhimurium displayed an elevated level of antimicrobial resistance and genome degradation compared to stool isolates. Meanwhile, transcriptome sequencing identified few genes that were differentially expressed between blood and stool isolates. Hierarchical clustering and principal component analysis did not effectively differentiate the expression profile of iNTS from non-iNTS. In summary, few genes could serve as reliable biomarkers to distinguish iNTS and non-iNTS at either the genomic or transcriptomic level. Nevertheless, iNTS has indeed accumulated subtle genomic differences from non-iNTS, which might contribute to invasiveness.

Morphological Characterization of Some Capia Pepper (Capsicum annuum L.) Genotypes

Capsicum annuum (Capia), a member of the Solanaceae family, has a rich genetic diversity in fruit and plant traits. To select Capia pepper genotypes with desirable morphological traits for the development of new hybrid capia pepper varieties, this study aimed to characterize genetic accessions generated from local populations, and standard and hybrid cultivars using morphological characters. One hundred and twelve genotypes were identified using 15 morphological traits (11 qualitative and 4 quantitative) and quantitative traits were measured while qualitative traits were observed. Cluster and Principal Component Analysis (PCA) was applied to determine the relationship between the lines identified as a result of single plant selection. As a result of the cluster analysis, a dendrogram was prepared to evaluate the morphological similarity between the lines, and nineteen groups were identified based on 15 variables. In Principal Component Analysis, six PC axes explained 66.2% of the total variation. As a result of the study, high morphological variability was observed among pepper lines. This evaluation of plant trait variability can assist geneticists and breeders in identifying populations with desirable characteristics for inclusion in pepper breeding programs.

Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions.

Background/Objectives: The emergence of antimicrobial resistance (AMR) due to the misuse and overuse of antibiotics has become a critical threat to global public health. There is a dire need to forecast AMR to understand the underlying mechanisms of resistance for the development of effective interventions. This paper explores the capability of machine learning (ML) methods, particularly unsupervised learning methods, to enhance the understanding and prediction of AMR. It aims to determine the patterns from AMR gene data that are clinically relevant and, in public health, capable of informing strategies. Methods: We analyzed AMR gene data in the PanRes dataset by applying unsupervised learning techniques, namely K-means clustering and Principal Component Analysis (PCA). These techniques were applied to identify clusters based on gene length and distribution according to resistance class, offering insights into the resistance genes' structural and functional properties. Data preprocessing, such as filtering and normalization, was conducted prior to applying machine learning methods to ensure consistency and accuracy. Our methodology included the preprocessing of data and reduction of dimensionality to ensure that our models were both accurate and interpretable. Results: The unsupervised learning models highlighted distinct clusters of AMR genes, with significant patterns in gene length, including their associated resistance classes. Further dimensionality reduction by PCA allows for clearer visualizations of relationships among gene groupings. These patterns provide novel insights into the potential mechanisms of resistance, particularly the role of gene length in different resistance pathways. Conclusions: This study demonstrates the potential of ML, specifically unsupervised approaches, to enhance the understanding of AMR. The identified patterns in resistance genes could support clinical decision-making and inform public health interventions. However, challenges remain, particularly in integrating genomic data and ensuring model interpretability. Further research is needed to advance ML applications in AMR prediction and management.

Unveiling Genetic Diversity in Grain Amaranth through K-Means Clustering and Principal Component Analysis

Grain amaranth (Amaranthus spp.) is a highly versatile and nutrient-dense pseudo-cereal that has gained increasing importance in global food systems. Developing diverse varieties and hybrids of grain amaranth (Amaranthus spp.) is essential for harnessing its full potential as a climate-resilient and nutritionally rich crop. Germplasm studies provide a comprehensive understanding of the genetic diversity within amaranth, facilitating the breeding of improved cultivars that combine desirable traits such as higher yield, enhanced nutritional content, and increased tolerance to biotic and abiotic stresses. Varietal development and hybridization can optimize characteristics like seed size, growth habit, and adaptability, making amaranth more suitable for a range of agroecological zones. In this direction present study conducted by evaluating 180 grain amaranth germplasm accessions for yield and contributing factors during the summer 2024 at the ICAR-IISS Regional Station in Bengaluru adjacent to NSP. Genetic distances among 180-grain amaranth germplasm accessions were determined using K-means clustering, resulting in 06 distinct groupings. Cluster V comprised of more number of accessions (48) and the least number of accessions in cluster VI (15). As per PCA, eight eigen values explains the total variance; among which PC1 explained maximum variance 82.06% followed by PC2, PC3, PC4, PC5, PC6, PC7 and PC8 explained 8.87%, 5.39%, 1.63%, 1.02%, 0.51%, 0.45% and 0.07 % respectively.

Geo-climatic factors co-drive the phenotypic diversity of wild hemp (Cannabis sativa L.) in the Potohar Plateau and Lesser Himalayas

Hemp (Cannabis sativa L.) is an annual, and dioecious herb belonging to the Cannabaceae family. This plant is native to Central and Southeast Asia. The wild races of this species are commonly growing in Khyber Pakhtunkhwa and Punjab provinces, as well as in Islamabad, Pakistan. This study provides crucial insights into how environmental variables influence the wild hemp populations, which can be utilized in for conservation and breeding. The present study was aimed at evaluating the effects of key environmental factors such as altitude, geographical location, precipitation, relative humidity, maximum, minimum, and average temperature on 16 morpho-agronomic traits of a wild population of hemp growing in the Potohar Plateau and Lesser Himalayas. Our findings indicated that high relative humidity (> 64%), low average temperature (< 15 °C), intermediate average temperature (19–22 °C), and high average temperature (> 22 °C) played significant roles in determining the distribution pattern of the wild hemp. Correlation analysis demonstrated that average annual temperature contributed a higher percentage of variation in phenotypic diversity than geographic variables. Additionally, cluster analysis indicated three groups for the selected 35 populations. Clustering and Principal Component Analysis (PCA) of the morpho-agronomic traits indicated that group 1 from the Lesser Himalayas showed high relative humidity (> 64%) and low average temperature (< 15 °C). Conversely, Group 2 populations from the Potohar Plateau demonstrated intermediate average temperature (19–22 °C). There is an existence of Group 3 in the Potohar Plateau with a high average temperature (> 22 °C) compared to Group 1 and Group 2. Our examination highlights the complex interplay between ecological factors, and morphological attributes in native landraces of Cannabis sativa, giving significant insight into knowledge for preservation and breeding initiatives. A study of genetic diversity could complement morpho-agronomic traits in future research to learn more about how genetic variation affects environmental adaptation.