Cluster Tree Research Articles

Pre-Service Teachers’ Assessment of ChatGPT’s Utility in Higher Education: SWOT and Content Analysis

ChatGPT (GPT-3.5), an intelligent Web-based tool capable of conducting text-based conversations akin to human interaction across various subjects, has recently gained significant popularity. This surge in interest has led researchers to examine its impact on numerous fields, including education. The aim of this paper is to investigate the perceptions of undergraduate students regarding ChatGPT’s utility in academic environments, focusing on its strengths, weaknesses, opportunities, and threats. It responds to emerging challenges in educational technology, such as the integration of artificial intelligence in teaching and learning processes. The study involved 257 students from two university departments in Greece—namely primary and early childhood education pre-service teachers. Data were collected using a structured questionnaire. Various methods were employed for data analysis, including descriptive statistics, inferential analysis, K-means clustering, and decision trees. Additional insights were obtained from a subset of students who undertook a project in an elective course, detailing the types of inquiries made to ChatGPT and their reasons for recommending (or not recommending) it to their peers. The findings offer valuable insights for tutors, researchers, educational policymakers, and ChatGPT developers. To the best of the authors’ knowledge, these issues have not been dealt with by other researchers.

ST-ADPTC: a method for clustering spatiotemporal raster data based on improved density peak detection

Spatiotemporal raster (STR) data employ an array of grids to represent temporally varying and spatially distributed information, commonly utilized for recording environmental variables and socioeconomic indices. To reveal the geographic patterns embedded in STR data, the clustering by fast search and finding of density peaks (CFSFDP) algorithm is considered effective and suitable. However, this algorithm encounters limitations in identifying cluster centers, handling large data volumes, and measuring the coupled spatial-temporal-attribute distance when applied to STR data. To overcome these challenges, we propose an improved method named spatial temporal-adaptive density peak tree clustering (ST-ADPTC). This method leverages adaptive density peak tree segmentation to identify cluster centers and optimizes memory usage through the k-nearest neighbors (kNN) technique. By constructing a neighborhood that incorporates both spatiotemporal and thematic attribute similarities, ST-ADPTC computes the local density of STR data, facilitating the discovery of time-varying clusters. Based on the proposed method, we develop an open-source Python package (Geo_ADPTC). Experiments conducted using benchmarking datasets illustrate improvements in cluster identification and memory reduction. Additionally, a case study of sea surface temperature data demonstrates the feasibility and effectiveness of exploring spatial and temporal distribution patterns using the proposed method.

Molecular evaluation and genetic behavior of white tip nematode resistance against Aphelenchoides besseyi in artificially infected rice plants

Genetic behavior of some morphological, yield and its related traits was studied under nematode infection condition using five rice genotypes which were crossed using complete-diallel analysis to produce 20 rice hybrids. All 25 genotypes were evaluated under both normal and white tip nematode infection conditions. Although, molecular evaluation using simple sequence repeats for characters related to resistance were done. As a result of this study, the parental varieties Giza179, Giza182 and Sakha105 were the best of all parents, since they possessed favorite general combining ability (GCA) effects for many traits. The three crosses Sakha106 × Sakha101, Sakha105 × Sakha101 and Giza182 × Giza179 recorded the desirable values of specific combining ability (SCA) for most of the studied traits. Dominance genetic variance played the most significant role in inheritance of the traits under study. The two crosses Sakha101 × Sakha106 and Sakha105 × Sakha101 were considered the best for yield and its components under affected condition by white tip nematode. RM5526 marker was the best in detecting genetic diversity among the rice accessions for the resistance-associated trait, no. Of Aphelenchoides besseyi (A. besseyi) individuals in 100 grains (NA). While, RM161 marker was the best in detecting genetic diversity among the rice accessions for the resistance-associated trait, loss rate of panicle weight (LRPW). Principal component analysis (PCA) was applied for morpho-physiological characters and molecular data analysis and showed a good separation of the genotypes which was in high agreement with phylogenetic tree clustering and also showed that SSR analysis can be recognized with the help of principal component analysis, which are accountable for the observed genotypic variations presented within each component. Two DNA fragments with molecular size of 165 bp and 146 bp may play an important role in the white tip nematode resistance in rice while they appeared only in the resistant parents and crosses. These bands should be isolated, sequenced and compared with the genes in data base which are responsible for white tip nematode resistance in rice. Both DNA fragments will be of great interest in nematode resistance programs in rice in the future. Finally, we can recommend the farmers to use the two varieties Giza 179 and Giza182 for planting in land affected with white tip nematode. The farmers could also be recommend to use the two hybrids Sakha106 × Sakha105 and Giza179 × Sakha105 when planting in land affected with white tip nematode while they proved to be resistant to white tip nematode by using morphological and molecular analyses. For researchers we can recommend by using the two specific molecular markers, RM5526 and RM161 for fast identification of white tip nematode resistant rice varieties in Egypt.

Monitoring the Dynamic Networks of Stock Returns with an Application to the Swedish Stock Market

AbstractIn this paper, two approaches for measuring the distance between stock returns and the network connectedness are presented that are based on the Pearson correlation coefficient dissimilarity and the generalized variance decomposition dissimilarity. Using these two procedures, the center of the network is determined. Also, hierarchical clustering methods are used to divide the dense networks into sparse trees, which provide us with information about how the companies of a financial market are related to each other. We implement the derived theoretical results to study the dynamic connectedness between the companies in the Swedish capital market by considering 28 companies included in the determination of the market index OMX30. The network structure of the market is constructed using different methods to determine the distance between the companies. We use hierarchical clustering methods to find the relation among the companies in each window. Next, we obtain a one-dimensional time series of the distances between the clustering trees that reflect the changes in the relationship between the companies in the market over time. The method from statistical process control, namely the Shewhart control chart, is applied to those time series to detect abnormal changes in the financial market.

The Integration of Dual Evaluation and Minimum Spanning Tree Clustering to Support Decision-Making in Territorial Spatial Planning

Spatial planning, a policy instrument for creating sustainable environments that meet the needs of the current and future generations, has been implemented extensively worldwide. However, it is difficult for urban planners to thoroughly determine the spatial value of a territory and make informed decisions regarding the efficient utilization of regional resources in the real world. This study proposes a novel methodological framework for spatial pattern optimization that can guide future land use by integrating Minimum Spanning Tree (MST) clustering with a comprehensive evaluation system (dual evaluation). Furthermore, the validity of this framework is demonstrated through a case study of territorial spatial planning in Deyang, China. The findings indicate that (1) the methodological framework presented in this study offers valuable guidance for the spatial arrangement of territorial resources, especially in practical projects; and (2) the combination of dual evaluation and MST clustering can facilitate automatic regionalization to identify spatial clusters exhibiting functional similarity in terms of land use. By focusing on methodological advancements, this study concludes that the integration of dual evaluation (DE) and MST clustering not only simplifies the identification of optimal land-use patterns but also promotes a more systematic and efficient approach to support spatial planning.

Development and verification of a 10K liquid chip for Hainan black goat based on genotyping by pinpoint sequencing of liquid captured targets

BackgroundChina has thousands years of goat breeding and abundant goat genetic resources. Additionally, the Hainan black goat is one of the high-quality local goat breeds in China. In order to conserve the germplasm resources of the Hainan black goat, facilitate its genetic improvement and further protect the genetic diversity of goats, it is urgent to develop a single nucleotide polymorphism (SNP) chip for Hainan black goat.ResultsIn this study, we aimed to design a 10K liquid chip for Hainan black goat based on genotyping by pinpoint sequencing of liquid captured targets (cGPS). A total of 45,588 candidate SNP sites were obtained, 10,677 of which representative SNP sites were selected to design probes, which finally covered 9,993 intervals and formed a 10K cGPS liquid chip for Hainan black goat. To verify the 10K cGPS liquid chip, some southern Chinese goat breeds and a sheep breed with similar phenotype to the Hainan black goat were selected. A total of 104 samples were used to verify the clustering ability of the 10K cGPS liquid chip for Hainan black goat. The results showed that the detection rate of sites was 97.34% -99.93%. 84.5% of SNP sites were polymorphic. The heterozygosity rate was 3.08%-36.80%. The depth of more than 99.4% sites was above 10X. The repetition rate was 99.66%-99.82%. The average consistency between cGPS liquid chip results and resequencing results was 85.58%. In addition, the phylogenetic tree clustering analysis verified that the SNP sites on the chip had better clustering ability.ConclusionThese results indicate that we have successfully realized the development and verification of the 10K cGPS liquid chip for Hainan black goat, which provides a useful tool for the genome analysis of Hainan black goat. Moreover, the 10K cGPS liquid chip is conducive to the research and protection of Hainan black goat germplasm resources and lays a solid foundation for its subsequent breeding work.

Interactions between the gut bacterial community of Exopalaemon carinicauda and infection by Enterocytozoon hepatopenaei

To explore the relationship between the intestinal flora of Exopalaemon Carinicauda and infection by Enterocytozoo Hepatopenaei (EHP), we analyzed the species and richness of gut microbiota in infected individuals in different EHP load groups [i.e., control (C), high load (H), and low load (L)] using gene sequencing after infection. The results showed that the abundance of intestinal flora in the high-load EHP group was significantly lower than that in the healthy group. Based on the UPGMA cluster tree and PCoA analysis, with comparisons to healthy shrimp, the gut microbiota of the EHP high load and low load groups were clustered into one branch, which indicated that EHP infection changed the composition of the gut microbiota of infected shrimps. The heat map analysis of species abundance clustering revealed that the dominant bacteria in the low EHP load group and the control group were beneficial genera such as Lactococcus, Ligilactobacillius, and Bifidobacterium, but the dominant bacteria in the high EHP load group were harmful genera such as Pseudomonas, Photobacterium, and Candidatus hepatincola. The functions of the intestinal flora predicted that most genes related to metabolism were more abundant in healthy shrimp, most genes related to metabolism and the organisms’ system were more abundant in the low EHP load group, and most genes related to diseases and environmental information processing were more abundant in the high EHP load group. After separation and purification, the dominant bacteria (Bifidobacterium animalis in healthy shrimp and Lactococcus garvieae in the low EHP load group) and the non-dominant bacteria (Macrococus caseolyticus in the low EHP load group) were obtained. Each of these isolated strains were used together with EHP to infect E. carinicauda, and the results showed that Bifidobacterium animali and Lactococcus garvieae significantly reduced the EHP load in EHP-infected individuals. At the same time, the morphology and structure of the hepatopancreas and intestinal tissue of EHP-infected E. carinicauda were improved. No improvement was seen in tissue that was infected with Macrococus caseolyticus.

Extraction and Analysis of Medicinal Property in Secondary Metabolites Obtained from Selected Plant Species

Medical plants have been used from thousands of years to treat and prevent many diseases. Wockhardt global school Consist various plants variety and for this study three medicinal plants were selected and these plants are Tridax procumbiens (Tantani), Ficus racemosa (Umbar), Nyctanthes abortiristis (Parijat). The primary aim of this study was to extract and identify the presence of phytochemicals and to determine their medicinal uses for human beings. From Vedic era the traditional plant medicines has been considered as a primary healthcare for human beings. In the given study the presence of flavonoids, alkaloids, N-Containing chemicals were determined by using phytochemical extraction method. The chemicals were identified based upon various qualitative tests. The selected plants consist glycosides, alkaloids, saponins, flavonoids and carbohydrates. This phytochemicals are responsible for anti-inflammatory properties hence the selected medicinal plants can be used for inflammation, bone pain, joint pain, swelling, and common cough etc. Keywords: Medicinal plants, secondary metabolites, phytochemicals, flavonoids, alkaloids, leaf extract.

UNSUPERVISED AIRWAY TREE CLUSTERING WITH DEEP LEARNING: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA) LUNG STUDY.

High-resolution full lung CT scans now enable the detailed segmentation of airway trees up to the 6th branching generation. The airway binary masks display very complex tree structures that may encode biological information relevant to disease risk and yet remain challenging to exploit via traditional methods such as meshing or skeletonization. Recent clinical studies suggest that some variations in shape patterns and caliber of the human airway tree are highly associated with adverse health outcomes, including all-cause mortality and incident COPD. However, quantitative characterization of variations observed on CT segmented airway tree remain incomplete, as does our understanding of the clinical and developmental implications of such. In this work, we present an unsupervised deep-learning pipeline for feature extraction and clustering of human airway trees, learned directly from projections of 3D airway segmentations. We identify four reproducible and clinically distinct airway sub-types in the MESA Lung CT cohort.

Hybrid Machine Learning Approach for predicting E-wallet Adoption among Higher Education Students in Malaysia

In today’s digitised world, e-wallets have been sprouting thick and fast in Malaysia as they contribute significantly to expediting onlinetransactions. The E-wallet system is not only a mechanism for businesses to acquire profit but is also one of the most secure paymentoptions for customers, particularly during the COVID-19 pandemic. However, the adoption of e-wallets among higher education students remains unfavourable, eliciting only minimal response. This study aims to analyse higher education students’ adoption of e-wallets using a hybrid machine learning method, combining clustering and decision trees. This approach provides deep insights into user behaviour, improving prediction accuracy and enabling personalised strategies for enhanced user experiences. It profiles and classifies students based on demographics and traits such as age, year of study, gender, frequency of use, future use intention, lifestyle compatibility, perceived trust, risk perception, convenience, and security factors. The analysis reveals the segmentation of the dataset into four distinct clusters, each characterised by shared attributes. These clusters are subsequently labelled descriptively and incorporated into the dataset. The dataset, now enriched with cluster information, serves as the foundation for constructing a decision tree model. The outcome of the decision tree indicates that Cluster 2 and Cluster 3 are hesitant towards e-payment. In contrast, Cluster 1 and Cluster 4 are more receptive despite security concerns, as e-wallets offer convenience despite lacking full trust, with security being a prominent concern amidst rising cyber threats. This study helps the Malaysian government and service providers promote cashless transactions and shape students’ financial independence based on their traits.

Price Prediction and Determination of the Affecting Variables of the Real Estate by Using X-Means Clustering and CART Decision Trees

The use of machine learning in real estate is quite new. When the working area is large, the factors affecting the price may vary according to the geographical regions and socioeconomic factors. It is thought that the price prediction performance of a model that will reflect these differences will be more successful than a general model. Unsupervised learning methods can be used both to increase performance and to show the variation of different factors affecting the price according to regions. With this aim, a hybrid model of X-Means clustering and CART decision trees was established in this study.  This model successfully learned the geographical and physical variables that affect the price. The prediction performance of the model was compared with the direct capitalization method, which is the gold standard in the domain. The hybrid model has a superior performance over direct capitalization in terms of mean square error, root mean square error and adjusted R-Squared metrics. The scores were 72.86, 0.0057 and 0.978, respectively. The effect of clustering was also examined. Clustering increased the prediction performance by 36%. 

Editorial

Dear Readers,  It gives me great pleasure to announce the fourth regular issue of 2024. I would like to thank all the authors for their sound research papers and the editorial board and our guest reviewers for their extremely valuable reviews and suggestions for improvement. These contributions and the generous support of the consortium members enable us to run our journal and maintain its quality. I would also like to thank our broader community for reading and incorporating sound J.UCS papers into their research. I am also very proud to announce that our journal has been included in the Free Journal Network as one of the listed high-quality journals in this field. Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to introduce six papers by 22 authors from nine countries: Brazil, Czech Republic, Germany, Iran, Jordan, Portugal, Thailand, Turkey, and the USA. In a collaboration between researchers from the Czech Republic and Germany, Sivaramasamy Elayaraja, Sunil Yeruva Vlastimil Stejskal, and Satish Nandipati look into multi-class microscopic image analysis of protozoan parasites using convolutional neural network and perform a study on 4740 microscopic images. André Diegues Fernandes, João Cruz, Miguel Mira da Silva, and Rúben Pereira from Portugal cover a systematic literature review for mapping and integrating security and risk standards. In a collaborative research between Jordan and the USA, Sahar Idwan, Junaid Zubairi, Syed Ali Haider, and Wael Etaiwi introduce their reactive traffic congestion control method based on a hierarchical graph approach. Shahrzad Riahi, Ramtin Khosravi, and Fatemeh Ghassemi from Iran conduct a study on knowledge-related policy analysis in an inference-enabled actor model. Josival Silva, Nelson Rosa, and Fernando Aires from Brazil research and present UP-Home, a self-adaptive solution that manages the security of smart homes by industry standards, and identifies and mitigates smart home security vulnerabilities. Last but not least, Sait Can Yucebas, Sukran Yalpir, Levent Genc, and Melike Dogan from Turkey discuss their research on price prediction and determination of the factors influencing real estate prices using X-Means clustering and CART decision trees. Enjoy Reading! Cordially, Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

One‐Pot Multicomponent Synthesis of Pyrano[2,3‐c]pyrazoles Facilitated by Novel Biocatalyst under Ambient Reaction Conditions

AbstractThis study depicts the synthesis of a novel biocatalyst Cluster Fig Ash (CFA) from cluster fig. XRD, FT‐IR, EDX, TGA, BET and SEM techniques were used to characterize the synthesized CFA catalyst. The one‐pot multicomponent synthesis of pyrano[2,3‐c]pyrazoles derivatives under ambient conditions was utilised to test the catalytic activity of this synthesized CFA biocatalyst. It was found that an easy, reliable, and extremely efficient method has been proposed to synthesise pyrano[2,3‐c]pyrazoles derivatives using one‐pot, multicomponent reaction involving aldehyde, malononitrile, ethylacetoacetate, and Hydrazine monohydrate.

Machine Learning Predicts the Level of Disease Spread

The aim of the research is predictive analysis of the spread of disease. Variable analysis at the population level in a region and the total disease events detected in the community. These variables can show the accuracy and certainty of the status of the resulting analysis. The concept of Machine Learning analysis is proposed to develop previous analysis models. The methods used include the K-Means cluster, Naïve Bayes, and Decision Tree (DT). There are two stages in the analysis process: pre-processing and classification. The discussion presented by K-Means provides a classification analysis pattern. The patterns obtained will be passed on to the classification process using Naïve Bayes and DT. Naïve Bayes results provide quite significant results with an accuracy rate of 83.33%. DT can also describe the results of information and knowledge analysis in the form of decision trees. DT produces decision trees that can provide knowledge and information analysis. The DT results provide an accuracy rate of 91.76% so these results can be used as consideration in decision making. The resulting information and knowledge can be used as a guide in making policies for handling health in the community.

Adaptively leverage multiple real-world data sources for treatment effect estimation based on similarity

ABSTRACT The incorporation of real-world data (RWD) into medical product development and evaluation has exhibited consistent growth. However, there is no universally adopted method of how much information to borrow from external data. This paper proposes a study design methodology called Tree-based Monte Carlo (TMC) that dynamically integrates patients from various RWD sources to calculate the treatment effect based on the similarity between clinical trial and RWD. Initially, a propensity score is developed to gauge the resemblance between clinical trial data and each real-world dataset. Utilizing this similarity metric, we construct a hierarchical clustering tree that delineates varying degrees of similarity between each RWD source and the clinical trial data. Ultimately, a Gaussian process methodology is employed across this hierarchical clustering framework to synthesize the projected treatment effects of the external group. Simulation result shows that our clustering tree could successfully identify similarity. Data sources exhibiting greater similarity with clinical trial are accorded higher weights in treatment estimation process, while less congruent sources receive comparatively lower emphasis. Compared with another Bayesian method, meta-analytic predictive prior (MAP), our proposed method’s estimator is closer to the true value and has smaller bias.

Molecular diversity of Klebsiella pneumoniae clinical isolates: antimicrobial resistance, virulence, and biofilm formation

One of the mechanisms responsible for antibiotic resistance in Klebsiella pneumoniae is the enzymes produced by the bacteria; another important mechanism is the ability to form biofilm. In this study, antibiotic resistance, genes associated with virulence, and biofilm-forming properties of K. pneumoniae strains were investigated. A total of 100 K. pneumoniae isolates were obtained from different clinical samples identified by Matrix-Assisted Laser Desorption/Ionization time-of-flight Mass Spectrometry. Antimicrobial susceptibility testing was performed with the Phoenix 100 apparatus. The biofilm forming properties of strains were determined by the microtiter plate method. For molecular analysis, genes encoding the carbapenemase enzyme (bla OXA-48, bla NDM-1, bla IMP, and bla VIM) and biofilm-related genes (treC, luxS, mrkA, and wza) were investigated by polymerase chain reaction (PCR). While 76% of clinical isolates were resistant to three or more antimicrobials, 24% were classified as non-multidrug resistant (non-MDR). When biofilm-forming capacities of clinical isolates were tested, it was determined that the resistant-isolates produced 59.2% strong biofilm, and susceptible-isolates produced 12.5% strong biofilm. According to PCR results, carbapenemase genes were determined as follows: bla OXA−48-70%, bla NDM-49%, and bla KPC-19%, bla OXA-48/bla NDM/bla KPC-12%, bla OXA-48/bla NDM-26%, and bla OXA-48/bla KPC-4%. The biofilm-associated genes in bacterial isolates were determined as follows: luxS-98%, treC-94%, mrkA-88%, and wza-15%. In addition, Hierarchical Clustering Tree and Heatmap analysis revealed an association between isolates that lacks resistance genes and isolates lacks biofilm-formation related genes that were included in MDR or non-MDR classes. As a result, biofilm should be considered in the treatment of MDR infections, and therapy should be planned accordingly. In addition, pursuing the data and genes of antibiotic resistance is significant for combating resistance.

Characterizing Deep Learning Package Supply Chains in PyPI: Domains, Clusters, and Disengagement

Deep learning (DL) frameworks have become the cornerstone of the rapidly developing DL field. Through installation dependencies specified in the distribution metadata, numerous packages directly or transitively depend on DL frameworks, layer after layer, forming DL package supply chains (SCs), which are critical for DL frameworks to remain competitive. However, vital knowledge on how to nurture and sustain DL package SCs is still lacking. Achieving this knowledge may help DL frameworks formulate effective measures to strengthen their SCs to remain competitive and shed light on dependency issues and practices in the DL SC for researchers and practitioners. In this paper, we explore the domains, clusters, and disengagement of packages in two representative PyPI DL package SCs to bridge this knowledge gap. We analyze the metadata of nearly six million PyPI package distributions and construct version-sensitive SCs for two popular DL frameworks: TensorFlow and PyTorch. We find that popular packages (measured by the number of monthly downloads) in the two SCs cover 34 domains belonging to eight categories. Applications , Infrastructure , and Sciences categories account for over 85% of popular packages in either SC and TensorFlow and PyTorch SC have developed specializations on Infrastructure and Applications packages, respectively. We employ the Leiden community detection algorithm and detect 131 and 100 clusters in the two SCs. The clusters mainly exhibit four shapes: Arrow, Star, Tree, and Forest with increasing dependency complexity. Most clusters are Arrow or Star, while Tree and Forest clusters account for most packages (Tensorflow SC: 70.7%, PyTorch SC: 92.9%). We identify three groups of reasons why packages disengage from the SC (i.e., remove the DL framework and its dependents from their installation dependencies): dependency issues, functional improvements, and ease of installation. The most common reason in TensorFlow SC is dependency incompatibility and in PyTorch SC is to simplify functionalities and reduce installation size. Our study provides rich implications for DL framework vendors, researchers, and practitioners on the maintenance and dependency management practices of PyPI DL SCs.

Semi-Supervised Predictive Clustering Trees for (Hierarchical) Multi-Label Classification

Semi-supervised learning (SSL) is a common approach to learning predictive models using not only labeled, but also unlabeled examples. While SSL for the simple tasks of classification and regression has received much attention from the research community, this is not the case for complex prediction tasks with structurally dependent variables, such as multi-label classification and hierarchical multi-label classification. These tasks may require additional information, possibly coming from the underlying distribution in the descriptive space provided by unlabeled examples, to better face the challenging task of simultaneously predicting multiple class labels. In this paper, we investigate this aspect and propose a (hierarchical) multi-label classification method based on semi-supervised learning of predictive clustering trees, which we also extend towards ensemble learning. Extensive experimental evaluation conducted on 24 datasets shows significant advantages of the proposed method and its extension with respect to their supervised counterparts. Moreover, the method preserves interpretability of classical tree-based models.

Three de novo assembled wild cacao genomes from the Upper Amazon

Theobroma cacao, the chocolate tree, is indigenous to the Amazon basin, the greatest biodiversity hotspot on earth. Recent advancement in plant genomics highlights the importance of de novo sequencing of multiple reference genomes to capture the genome diversity present in different cacao populations. In this study, three high-quality chromosome-level genomes of wild cacao were constructed, de novo assembled with HiFi long reads sequencing, and scaffolded using a reference-free strategy. These genomes represent the three most important genetic clusters of cacao trees from the Upper Amazon region. The three wild cacao genomes were compared with two reference genomes of domesticated cacao. The five cacao genetic clusters were inferred to have diverged in the early and middle Pleistocene period, approximately 1.83–0.69 million years ago. The results shown here serve as an example of understanding how the Amazonian biodiversity was developed. The three wild cacao genomes provide valuable resources for studying genetic diversity and advancing genetic improvement of this species.

Phytochemical investigation and experimental evaluation of Panchavalkala for Lekhana Karma w.s.r. to Antihyperlipedemic Activity

Panchavalkala the barks of five trees i.e., Nyagrodha (Ficus benghalensis L.), Udumbara (Ficus racemosa L.), Ashwatha (Ficus religiosa L.), Plaksha (Ficus virens Aiton) and Parisha (Thespesia populnea (L.)Sol.ex Correa) are also known as Pancha Ksheeri Vrikshas in use since Vedic period. The plant samples were collected in Sharad Rutu (late autumn) from their natural habitat, extracted using ethanol and water and subjected for the phytochemical analysis. The study was aimed to evaluate Antihyperlipidemic effect (Lekhana Karma) of Panchavalkala Bark extracts individually and in combination (1:1:1:1:1) both in ethanolic and aqueous extracts on serum lipid profile in Cafeteria died (HFD) induced hyperlipidemia in SD rats. The animals were divided into fifteen groups of six animals each. The normal control group continued to be fed laboratory pellet chow ad libitum. The cafeteria diet-control group received the cafeteria diet in addition to the normal pellet diet (NPD). The remaining 13 groups were fed with the cafeteria diet and NPD along with standard control Atorvastatin 10mg/kg and the test drug p.o for 30 days. Treatment with the test substance PVK AE and PVK EE at doses of 200mg/kg body weight p.o. controlled and regulated the HFD induced weight gain, glucose and lipids in the blood when compared against high fat diet control (P< 0.05). PVK bark extracts are capable of exhibiting significant antihyperlipidemic activity in HFD induced SD rats. Taken together, this study strongly suggests that PVK bark extracts might be an efficient way for treatment of hyperlipidemia.