Average Linkage Research Articles

Beyond vacuolar pathology: Multiomic profiling of Danon disease reveals dysfunctional mitochondrial homeostasis.

Figure S1: Visual representation of the interaction network of dysregulated proteins. String analysis was carried out separately for increased and decreased proteins. Increased proteins (left panel) show a strong functional interdependence for mitochondrial proteins, whereas decreased proteins (right) form a cluster of proteins involved in protein translation and degradation. The interaction network was built with String 10 DB. Figure S2: (A) Protein investigation: DD-patients (n=2) versus Controls (n=3) => 1,606 unique proteins included from which on average 2 or more peptides were identified in the DD-patient as well as in the control group. Rows are centered; unit variance scaling is applied to rows. Both rows and columns are clustered using correlation distance and average linkage. For all proteins an average of at least 2 peptides had to be identified in each sample. 184 rows, 5 columns, T-test between DD-patients and Controls p<0.05 (B) mRNA investigation: DD-patients (n=2) versus Controls (n=4) => 14,334 mRNA species included. Rows are centered; unit variance scaling is applied to rows. Both rows and columns are clustered using correlation distance and average linkage. 543 rows, 6 columns. FPKM>0.1, T-test between DD-patients and Controls p<0.001. Figure S3: Co-localization of mitochondria with the activated p62-pathway. Immunofluorescence with co-localization (yellow) of mitochondrial protein COX-II (red) and p62 (SQSTM1; green). DAPI staining of nuclei in blue (magnification x400). Table S1. Supporting Information Table S2. Supporting Information Table S3. Supporting Information Table S4. Supporting Information Table S5. Supporting Information Table S6. Supporting Information Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Improved Genetic Map and Localization of Quantitative Trait Loci for Quality Traits in Auricularia heimuer

Auricularia heimuer is among China’s most important edible mushrooms and is rich in gum. With the improvement of people’s quality of life, demand is increasing for high-quality and good-tasting food; thus, the texture of A. heimuer is the focus of increasing attention. In this study, we added extra markers to a previously constructed genetic linkage map to generate a high-density genetic linkage map of A. heimuer, resolved the attributes of substrate quality-related traits, and performed quantitative trait locus (QTL) localization. The original genetic linkage map was improved by adding two new linkage groups, merging seven linkage groups into three linkage groups, and increasing the average linkage distance and total linkage estimated length. We anchored the 142 scaffolds of the genome to the improved genetic linkage map. In total, 15 significant QTLs controlling four quality-related traits were detected. Gumminess and chewiness, and cohesiveness and resilience, were linked. Three genes controlled cohesiveness and resilience; one gene controlled gumminess and chewiness. In conclusion, this study lays the foundation for gene localization and chromosome assembly in A. heimuer, elucidation of the mechanism of substrate quality-related trait formation, and provides a basis for precision breeding of A. heimuer.

APPLICATION OF TIME SERIES CLUSTER ANALYSIS IN CLUSTERING THE CENTRAL JAVA PROVINCE BASED ON THE POVERTY DEPTH INDEX

Poverty is a problem that continues to be faced, especially in developing countries such as Indonesia. Poverty is included in one of the Sustainable Development Goals (SDGs) programs, which is related to hunger and health. The time series data can be clustered based on the characteristics of the time series data and adjusted to the time series pattern. The choice of distance and method used must be adjusted to the dynamic structure of time series data. The purpose of this research is to cluster districts/cities in Central Java Province based on the poverty depth index value from 2017 to 2022. The variable that used in this research is the Poverty Depth Index of 35 districts in Central Java Province from 2017 to 2022. This research used cluster time series with DTW similarity measurment. Based on theDTW and cophenetic coefficient correlation value using three linkage methods, the average linkage method has the highest cophenetic coefficient correlation value of 0.8017988. Testing the quality of clusters using the silhouette coefficient using DTW distance and average linkage method and 2 clusters are included in the good cluster category with a silhouette coefficient value of 0.60. The resulting clusters using the DTW distance and average linkage method are cluster 1 consisting of 25 districts / cities and cluster 2 consisting of 10 districts.

HIERARCHICAL CLUSTER ANALYSIS ON PEOPLE'S WELFARE IN SOUTHEAST SULAWESI PROVINCE

Problems with people's welfare typically result from the government's development efforts in a region not being done properly or not being done equally. Consider grouping and defining the traits of each region's degree of welfare as a potential answer to ensure that development policies and strategies are well-targeted. This study aims to classify 17 regency/cities in Southeast Sulawesi province based on several indicators of people's welfare. The method used is hierarchical cluster analysis with several approaches, including Single Linkage, Complete Linkage, Average Linkage, and Ward's. The data used in this study is secondary data obtained from the publication of the Central Agency of Statistics (CAS) of Southeast Sulawesi Province. Based on the results of the evaluation the best method used is Ward's method which produces three clusters. The first cluster consists of 9 regencies, namely Buton, North Buton, South Buton, Central Buton, Muna, West Muna, Wakatobi, Konawe Islands, and East Kolaka, the majority of which come from the archipelago. Some of the problems that occur in these areas are the relatively high poverty rate and the low average length of schooling and life expectancy. The same thing happened to the second cluster which consisted of 6 regencies, namely Konawe, South Konawe, North Konawe, Bombana, Kolaka, and North Kolaka with problems of poverty, the average length of schooling, and relatively low sources of proper drinking water when compared to other clusters. The third cluster consists of 2 urban areas, namely Kendari City and Baubau City, the problems that occur are the relatively high unemployment rate and population density. The government ought to offer more initiatives to handle issues with poverty, education, and health in regions in clusters 1 and 2. While in cluster 3, the government ought to offer more initiatives to combat jobless issues and prepare for rising population densities.

PENERAPAN ANALISIS KLASTER HIERARKI UNTUK PENGELOMPOKAN KABUPATEN/ KOTA DI PROVINSI MALUKU BERDASARKAN STATUS PENDIDIKAN

One of the main goals of education is to develop potential and to educate individuals. Education is an effort to help the souls of students both physically and mentally towards a better human civilization. This research was conducted to group districts/cities in Maluku province based on educational status using hierarchical cluster analysis with the single linkage, average linkage and ward's methods. The results showed that clustering using the single linkage and average linkage methods was almost the same, while clustering using the ward's method had quite different results. Clustering using the single linkage and average linkage methods shows that Ambon city is the only city that always has its own cluster and does not join other districts/cities. This shows that the city of Ambon has an education status that is quite different from other districts/cities in Maluku Province. Regencies/cities that are always in the same cluster, namely cluster 1, both in the single linkage method, average linkage and the ward's method are Tanimbar Islands Regency, Southeast Maluku Regency, Central Maluku Regency and Tual City

Insight into the mechanisms for pyrolyzing organic macromolecular structure of subbituminous coal by comparing the extraction and flash pyrolysis

Investigating the organic structure of coal is an essential basic research for producing clean fuels and value-added fine chemicals by the clean and efficient utilization of coal. As a consequence, the structural characteristics of organic macromolecular in subbituminous coal (SBC) were analyzed at different levels using multiple destructive and non-destructive technical strategies. Correspondingly, the pyrolysis mechanisms of SBC organic macromolecular structure were investigated to understand through the molecular level correlation group component from extraction portion (EP) and flash pyrolysis (FP). In detail, solid-state 13C nuclear magnetic resonance analysis indicates that carbon skeleton of SBC mainly consists of aliphatic carbons (48.5%) and aromatic carbons (50.3%). The number of substituents on each aromatic ring in SBC is up to 2 on average and aliphatic linkages connect 3–4 aromatic rings. X-ray photoelectron spectrometer analysis exhibits that the >C–O- groups, pyrrolic, and sulfonic sulfurs of organic components are the most abundance O-, N-, and S-containing species, which is consistent with Fourier transform infrared and solid-state 13C nuclear magnetic resonance. The destructive analysis with thermogravimetric implies that organic macromolecular structure in SBC consists different types of covalent bonds (>Cal-X) with different strength. Furthermore, the contents of chain alkanes (CAs, 17.4%), arenes (62.7%), and oxygen-containing organic species (OCOSs, 18.0%) in EP from SBC (EPSBC), while the CAs (53.1%), alkenes (7.4%), arenes (27.3%), and OCOSs (7.5%) in FP of SBC (FPSBC). Comprehensive comparative analysis of detectable small molecule species from EPSBC and FPSBC further indicate that the cleavage > Cal-X bridge bonds of organic macromolecular structure in SBC attack by active hydrogen (H‧) through the pyrolysis reaction to obtain more alkenes, CAs, alkanols, and phenols.

Selection of an adequate functional diversity index for stream assessment based on biological traits of macroinvertebrates

Functional diversity (FD) is useful for the evaluation of freshwater ecosystems. The FD of macroinvertebrate families for river water quality (WQ) assessment in the Paute River Basin (PRB), Ecuador, was investigated. Macroinvertebrate samples and data about 26 physical, chemical, microbiological and hydro-geomorphological variables were available. Literature-based biological traits were allocated as scores to the macroinvertebrates data through fuzzy coding. The Generalised Additive Mixed Model (GAMM) was used to assess the performance of six FD indices using the referred 26 WQ descriptive variables. The best performing GAMM led to selecting the index based on functional dendrograms including the species community pool (wFDc) as the most suitable to characterise FD in the PRB. The sub-basins of the PRB were grouped in three classes applying Average Linkage Clustering (ALC) and using wFDc. The Random Forest (RF) algorithm was used with a global efficiency of 89% to assess the ALC clusters consistency and pre-identify the significant WQ descriptive variables, explaining most of the FD variability. The Kruskal-Wallis test was then applied to refine the outcomes of the previous analysis. Twelve WQ descriptive variables were finally identified as the best discriminant predictors for FD, including the riparian vegetation, electric conductivity, dissolved oxygen, total hardness, faecal coliforms and pH. It is believed that the implemented approach successfully assessed the stream WQ status of the PRB upon selecting a suitable macroinvertebrate FD index; as such, it could be applied to other tropical basins for WQ assessment.

MP24-02 CLUSTER ANALYSIS IDENTIFIES BENIGN PROSTATIC HYPERTROPHY CLINICAL PHENOTYPES

You have accessJournal of UrologyCME1 Apr 2023MP24-02 CLUSTER ANALYSIS IDENTIFIES BENIGN PROSTATIC HYPERTROPHY CLINICAL PHENOTYPES Matthew Simmons, Nathaniel Taylor, W Carter Reed, Pablo Santamaria, Zachary Klaassen, Sherita King, and Martha Terris Matthew SimmonsMatthew Simmons More articles by this author , Nathaniel TaylorNathaniel Taylor More articles by this author , W Carter ReedW Carter Reed More articles by this author , Pablo SantamariaPablo Santamaria More articles by this author , Zachary KlaassenZachary Klaassen More articles by this author , Sherita KingSherita King More articles by this author , and Martha TerrisMartha Terris More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003249.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Benign prostatic hypertrophy (BPH) is a heterogeneous disease that is affected by multiple variables including infection, genetics and inflammation. This study assessed whether hierarchical cluster analysis could better characterize BPH patients. METHODS: Patients who underwent TURP or simple prostatectomy between January 2017 to August 2022 were reviewed. 92 patients who had CT imaging within 18m of surgery were selected. CT-based prostate volumes (PV) were calculated using the ellipsoid volume formula (L*W*H*(π/6)). Metabolic syndrome score (MetX) was calculated by adding normalized BMI score to the number of metabolic syndrome-related diseases present (i.e., hypertension, hyperlipidemia, type II diabetes mellitus each disease given a score of 1). Data were scaled using Z transformation. Hierarchical clustering was conducted with Heatmapper software using average linkage and Pearson distance measurement methodology. RESULTS: Mean age of the cohort was 68 (SD=8.5), and 40% were African American. Hypertension, hyperlipidemia and type II diabetes mellitus were present in 73%, 58% and 32% of patients, respectively. Mean prostate volume was 84 ml (SD=56). Median lobes (ML) were present in 26%. Intraprostatic calcifications (IPCs) were present in 37%. Clustering revealed three clear groupings. Group 1 cnsisted of men with IPCs. These men had lower than average age, PV, and MetX scores. Group 2 consisted of men with either MLs or high PV without concomitant high MetX score. Two categories of men were included in Group 2 - younger than average men with MLs; and older than average men men with high PVs. Group 3 consisted of men with high MetX score. Age and PV was lower than average in this group. CONCLUSIONS: Cluster analysis identified three distinct clinical phenotypes of BPH. Classification of BPH subtypes may allow for improved diagnosis and therapy. Future studies will correlate molecular subtyping data and clinical outcomes data to further validate findings. Source of Funding: Medical College of Georgia © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e317 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Matthew Simmons More articles by this author Nathaniel Taylor More articles by this author W Carter Reed More articles by this author Pablo Santamaria More articles by this author Zachary Klaassen More articles by this author Sherita King More articles by this author Martha Terris More articles by this author Expand All Advertisement PDF downloadLoading ...

Applications of Energy Dispersive X‐Ray Fluorescence Spectrometry and Direct Current Arc Atomic Emission Spectroscopy Methods for Grouping Study of Automobile Windshield Glasses for Glass Forensics

AbstractA comprehensive evaluation was carried out for elemental characterization of automobile glasses having potential forensic application by Energy Dispersive X‐ray Fluorescence (ED‐XRF) spectrometry and Direct Current Atomic Emission Spectroscopy (DC Arc AES). The methods were optimized and validated using geological certified reference materials and glass standard reference material. Total seventeen elements including five major elements (Si, Na, Ca, Mg and Fe) and twelve minor and trace elements (Al, Ti, V, Cr, Mn, Ni, Cu, Zn, Rb, Mo, Zr and Sr) were found to be present. Though DC Arc AES performed better for minor and trace level determination, however, the precision was evaluated as 15–20 % RSD. On the contrary, ED‐XRF performed better for major elements with 5–10 % RSD. The detection limits for the analytes prone to form refractory oxides on heating were found to be poorer in DC Arc AES compared to ED‐XRF. Statistical analyses were also performed to evaluate the reliability of the data obtained by the optimized methods. As a part of forensic applications, the elemental concentrations of minor and trace elements were utilized for the grouping study of the glass samples by cluster analysis using average linkage method.

Combining performance and estimated genetic diversity among soybean parents and F1 populations

Studies about the combining ability and genetic diversity can provide information on the genetic resources contained in the working collection of germplasm. The objective of this study was to estimate the combining ability of ten soybean parents and their hybrids in the F1 generation using diallel analysis and genetic diversity analysis to evaluate the dissimilarity between parents and crosses. The experiment was carried out in a greenhouse, in a completely randomized design with five replications, during the 2019/2020 crop season, in the municipality of Rio Verde, State of Goiás, Brazil. Agronomic and physiological variables were evaluated. Data were subjected to analysis of variance. Diallel analysis was performed using the Griffing method 2. Analysis of genetic diversity was performed with the Mahalanobis generalized distance and the hierarchical average linkage clustering. Additive gene effects were predominant in the control of all variables analyzed in the F1 generation. The parents SYN 1163 RR and CG 7370 RR showed good complementarity for the variable grain yield. The thirty-five genotypes were separated into six groups, and the variable weight of one hundred seeds had the greatest contribution to genetic diversity.

Avena sterilis L.’in Mikrosatellit İşaretleyicileri ile Genetik Çeşitlilik Analizi

Avena sterilis is an important grassy weed that causes a problem in wheat cultivation areas, and intensive herbicides are used to control it. Genetic diversity study is important to have information how on the effect weed species of the environmental factors and herbicides. The high level of genetic diversity within the species is a guarantee for adaptation to changing environmental conditions. This study was conducted based on complaints of the loss of effectiveness during the chemical control. Analyses were carried out on 27 selected populations collected from wheat cultivation areas in Turkey's Middle Black Sea region. Seventy-seven alleles from microsatellite loci were detected, and the average number of alleles per locus was determined as five using 17 microsatellite primers. The average gene diversity (GD) and polymorphism information content (PIC) values were 0.894 and 0.732, respectively. According to the UPGMA dendrogram generated using Average Linkage, the populations were clustered into two main groups. A high degree of diversity was found among the studied steril oat genotypes, and it is seen that there is no geographical isolation. It is understood that this situation primarily provides adaptation to those geographical areas by transporting weed seeds from other provinces in various ways. According to these findings, issues such as crop rotation, cultural control methods, encouraging the use of certified seeds, and the use of herbicides with different mechanisms of action can be listed among the measures that can be taken to control the high genetic diversity, which is an indicator of herbicide resistance.

Yield Estimation of Dolichos Bean (Lablab purpureus L.) Using Different Clustering Techniques

Dolichos bean (Lablab purpureus L.) is an important leguminous vegetable crop. Diversity of genes to a high extent in the gene pool provides better opportunities to develop appropriate plant varieties. Hence, finding advantageous traits or their combinations is the key step. Genetic diversity is important for parents to be chosen for potential breeding programmes. In general, genetically divergent parents are used in segregating generations to produce the optimal recombinants. Green seed yield of the crop is influenced by number of yield contributing characters such as, green pod yield, pods per plant, days to 50 percent flowering, number of primary branches, raceme per plant, nodes per raceme, raceme length and 100 fresh seed weight. The association between the characters and yield provides basis for further breeding programme. Cluster analysis was used to classify the germplasm accessions into different clusters based on the morphological traits of the accessions. The data on germplasm accessions (64) was collected from the Department of Genetics and Plant Breeding, UAS, GKVK, Bengaluru, Karnataka. Euclidean distance metric was used to measure the difference between germplasms. Elbow method indicated three optimum genotype clusters. Three clustering methods were used viz., single, complete and average linkage. Dunn and Silhouette indices were used to validate the clusters. It was found that Average linkage method was best among the three. Further, in the third cluster by average linkage method with 12 genotypes was found to have highest green seed yield and green pod yield. Hence these 12 genotypes can be used for further breeding programmes.

3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection.

In this current era, biomedical big data handling is a challenging task. Interestingly, the integration of multi-modal data, followed by significant feature mining (gene signature detection), becomes a daunting task. Remembering this, here, we proposed a novel framework, namely, three-factor penalized, non-negative matrix factorization-based multiple kernel learning with soft margin hinge loss (3PNMF-MKL) for multi-modal data integration, followed by gene signature detection. In brief, limma, employing the empirical Bayes statistics, was initially applied to each individual molecular profile, and the statistically significant features were extracted, which was followed by the three-factor penalized non-negative matrix factorization method used for data/matrix fusion using the reduced feature sets. Multiple kernel learning models with soft margin hinge loss had been deployed to estimate average accuracy scores and the area under the curve (AUC). Gene modules had been identified by the consecutive analysis of average linkage clustering and dynamic tree cut. The best module containing the highest correlation was considered the potential gene signature. We utilized an acute myeloid leukemia cancer dataset from The Cancer Genome Atlas (TCGA) repository containing five molecular profiles. Our algorithm generated a 50-gene signature that achieved a high classification AUC score (viz., 0.827). We explored the functions of signature genes using pathway and Gene Ontology (GO) databases. Our method outperformed the state-of-the-art methods in terms of computing AUC. Furthermore, we included some comparative studies with other related methods to enhance the acceptability of our method. Finally, it can be notified that our algorithm can be applied to any multi-modal dataset for data integration, followed by gene module discovery.

CCNB2 as a potential biomarker of bladder cancer via the high throughput technology.

Bladder cancer and oral squamous cell carcinoma (OSCC) seriously affect people's health. However, the relationship between bladder cancer and OSCC remains unclear. Got GSE138206, GSE146483, GSE184616, and bladder cancer datasets GSE65635, GSE100926 from Gene Expression Omnibus database. Weighted gene co-expression network analysis was used to identify the significant module. Functional enrichment analysis was performed via the Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes. Furthermore, the Gene Set Enrichment Analysis was also used to complete the enrichment analysis. Comparative Toxicogenomics Database found most relevant diseases to core genes. TargetScan is used to forecast analysis of microRNA and target genes. In Gene Ontology analysis, differentially expressed genes were mostly concentrated in cell differentiation, extrallular region, structural molecule activity, and actin binding. In Kyoto Encyclopedia of Genes and Genomes analysis, the differentially expressed genes were mainly enriched in PI3K-Akt signaling pathway, pathway in cancer, and extracellular matrix-receptor interaction. Seven hub genes (cyclin B2 [CCNB2], TK1, CDC20, PCNA, CKS1B, CDCA5, MCM4) were obtained. Hub genes (CCNB2, CDC20) are highly expressed in OSCC and bladder cancer samples. CCNB2 was one common oncogene of bladder cancer and OSCC.

Dual Mach–Zehnder Interferometers With Hierarchical Clustering Analysis Method Applied for Positioning the Intrusion

The method of hierarchical clustering is for the first time employed to locate the intrusion-induced disturbance on the sensing fibers of a dual Mach–Zehnder interferometer (DMZI). Such an intrusion-induced disturbance is located by finding the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${x}$ </tex-math></inline-formula> coordinate of the centroid of the largest cluster on the Euclidean plane through hierarchical clustering with an appropriate linkage criterion employed for determining the distance between two observations. We compare average linkage and complete linkage criteria in the clustering analysis to see which one provides better locating accuracy. In the clustering analysis, the number of clusters is set to be 3–8 in finding the location of disturbance. To reduce the locating error, we also use differential signals here in the clustering analysis. Twelve intrusion events are simulated by knocking the sensing fibers to induce disturbances at a given location. The location of disturbance is determined through the clustering analysis for each intrusion event. The mean of the absolute values of locating errors [mean absolute error (MAE)] for the 12 intrusion events is then estimated. The experimental results in this study demonstrate a maximum MAE of 11.55 m in locating an intrusion with average linkage criterion employed for five-cluster analysis. Also, the MAE could be 3.55 m smaller by using the differential signals for clustering analysis, compared with the case when directly detected signals are used for clustering analysis. The results also confirm that the average linkage criterion provides only a small amount of improvement in MAE over complete linkage criterion.

The fluctuation linkages and price volatility risk on agricultural commodity market: Evidence from Vietnamese coffee

This paper uses the DCC-GARCH and Value at Risk (VaR) model to analyze the fluctuation, linkage, and price volatility risk among coffee price series in the period of 2004 - 2020. In terms of the fluctuation, the study points out, the volatility of Vietnamese coffee price and the price of Robusta coffee in two markets were affected by two ARCH terms and GARCH terms at 1 percent level. Meanwhile, the coffee price of Brazil and Colombia is only impacted by the ARCH term. The linkage between Brazil and Colombia is the biggest. The average coefficient linkage among Vietnam with two main competitors is relatively small. In terms of price volatility risk, the price volatility risk of Vietnamese coffee is the smallest and the biggest risk is belonging to Brazilian coffee price. The results obtained would be a valuable reference for stakeholders, policymakers, coffee processing and exporting enterprises, and coffee farmers to clearly understand the fluctuation and linkage among coffee export price series, and thereby have appropriate and effective solutions and strategies in price volatility risk management to sustainable development.

Agro Morphological Characterization and Diversity Assessment of Pea (Pisum Sativum L.) Germplasm Conserved in Genebank of Nepal

A total 122 landraces collection collection of pea (Pisum sativum L.) germplasm accessions conserved in the Gene bank of Nepal was characterized for 8 quantitative and 11 qualitative traits to assess the magnitude of prevailing genetic variability at National Agriculture Genetic Resources Centre (Genebank), Khumaltar, Nepal during 2017. Shannon and Weaver diversity index (H’) analysis revealed significant intra landrace diversity for both quantitative (0.783) and qualitative traits (0.536). The first two principle components with Eigen value &gt;1 accounted for 66.3% of the entire variability for quantitative traits. For qualitative traits, four principle components with Eigen value &gt;1 accounted for 61.6% of the entire variability. Four clusters were established with phenotypic similarities using Euclidean distance and average linkage methods. The landraces grouped into the cluster I were characterized by higher pod length, width and 100 seed weight. Pearson correlation analysis among seven quantitative traits showed highly significant positive correlation (r = 0.557***) between flowering days and maturity days. This variation indicated that there is a way to identify promising genotypes for pea breeding.

Estimation of Linkage Disequilibrium, Effective Population Size, and Genetic Parameters of Phenotypic Traits in Dabieshan Cattle.

Dabieshan cattle (DBSC) are a valuable genetic resource for indigenous cattle breeds in China. It is a small to medium-sized breed with slower growth, but with good meat quality and fat deposition. Genetic markers could be used for the estimation of population genetic structure and genetic parameters. In this work, we genotyped the DBSC breeding population (n = 235) with the GeneSeek Genomic Profiler (GGP) 100 k density genomic chip. Genotype data of 222 individuals and 81,579 SNPs were retained after quality control. The average minor allele frequency (MAF) was 0.20 and the average linkage disequilibrium (LD) level (r2) was 0.67 at a distance of 0-50 Kb. The estimated relationship coefficient and effective population size (Ne) were 0.023 and 86 for the current generation. In addition, we used genotype data to estimate the genetic parameters of the population's phenotypic traits. Among them, height at hip cross (HHC) and shin circumference (SC) were rather high heritability traits, with heritability of 0.41 and 0.54, respectively. The results reflected the current cattle population's extent of inbreeding and history. Through the principal breeding parameters, genomic breeding would significantly improve the genetic progress of breeding.

Evaluation of Different Clustering Techniques in Classifying the Vegetable Growing Panchayats of Ernakulam District, Kerala

The goal of this study was to evaluate different clustering techniques in classifying the vegetable growing locations of Ernakulam (EKM) district of Kerala so that same nutrient recommendation can be prescribed for panchayats coming under the same cluster. Hierarchical clustering (HC) and K –means clustering were performed to group the panchayats based on soil fertility status and thereafter comparison of various clustering procedures was done using Davies – Bouldin (DB) index. Different dissimilarity measures- Euclidean, squared Euclidean, Chebychev distance and Mahalanobis D2 were determined and single linkage, complete linkage and average linkage methods were adopted under these measures. The results revealed that Mahalanobis D2 was the better clustering procedure with seven clusters (DB index: 0.120) followed by average linkage method under Euclidean distance (DB index: 0.306) with seven clusters. Manjapra and Keerampara panchayats remained as individual clusters. Keerampara had strongly acidic soils (pH -5.17) with high available Mg (158 mg kg-1) while Manjapra soils had low Mg availability (19 mg kg-1) and high S content (57 mg kg-1). Kakkad, Kalady and Vengoor came under cluster I which possessed approximately same EC (0.15-0.19 dS m-1), OC (2-2.4%) and Mg (71-73 mg kg-1) content. Chengamanadu and Vengola came under cluster III while Ayyampuzha and Mudakkuzha belong to cluster IV.

Analisis Hierarchical Clustering Multiscale Bootstrap (Kasus: Indikator Kemiskinan di Provinsi Sulawesi Selatan Tahun 2020)

Hierarchical cluster analysis is a statistical analysis used to group data based on their similarities. The single linkage, complete linkage and average linkage methods can be used to group data using distance techniques. There is a large difference in the number of poor people in urban and rural areas in South Sulawesi Province, so an analysis is needed to classify areas that have the same characteristics based on poverty indicators. For this reason, these three methods are used. However, the results of this analysis are only based on the similarity measure based on the distance technique used. Thus, the multiscale bootstrap method is used to obtain the validity of the resulting clusters. The results of the research using these three methods are four clusters with different characteristics. By using multiscale bootstrap, it is found that in single linkage there are four valid clusters, for complete linkage there is only one valid cluster and on average linkage there are three valid clusters. So it is found that single linkage is the best method in classifying these cases.