Analisis Cluster Penyakit Malaria Provinsi Papua Menggunakan Metode Single Linkage Dan K-Means

Malaria adalah penyakit yang disebabkan oleh parasite bernama Plasmodium. Tercatat keseluruhan kasus malaria yang terjadi di Indonesia pada tahun 2019 adalah sebanyak 250.644 kasus. Dan kasus malaria tertinggi terjadi di provinsi Papua, yaitu sebesar 86% atau sebanyak 216.380 kasus. Di Provinsi Papua, penyakit malaria dialami oleh semua usia dan bulan-bulan terjadi peningkatan pasien penderita malaria juga sangat bervariasi. Hal ini mengakibatkan dinas Kesehatan mengalami kesulitan dalam mengelompokan jenis malaria berdasarkan usia pasien dan bulan-bulan kejadian. Sebenarnya sudah ada penelitian yang menjelaskan pengelompokan jenis-jenis malaria, namun belum dijelaskan secara terperinci masing-masing kelompok malaria seperti Malaria Tropika, Malaria Tertiana, Malaria Quartana, Malaria Ovale. Tujuan dari penelitian ini adalah, melakukan analisis cluster terhadap beberapa jenis malaria, usia dan bulan kejadian. Metode cluster yang digunakan dalam penelitian ini adalah metode Single Linkage dan K-Means. Selanjutnya kedua metode akan di evalusi menggunakan standar deviasi. Metode terbaik yang dapat digunakan untuk analisis cluster adalah metode yang memiliki nilai standar deviasi lebih kecil. Data yang digunakan dalam penelitian ini adalah data sekunder yang diperoleh dari Dinas Kesehatan Provinsi Papua. Hasil penelitian menunjukan bahwa, metode Single Linkage lebih akurat dibandingkan dengan K-Means. Dimana dari 50 pasien terdapat 47 pasien lebih dominan terkena penyakit malaria tertiana yaitu pada rentang usia remaja dan dewasa pada bulan juni. Sehingga diharapkan pemerintah Provinsi Papua dapat memberikan sosialisasi kepada masyarakat, khususnya mereka yang pada rentang usia remaja dan dewasa. Karena hampir 94% penyakit malaria tertiana di derita oleh mereka yang berusia remaja dan dewasa.

A DNA barcode library for the water mites of Montenegro

Water mites (Acari, Hydrachnidia) are a significant component of freshwater ecosystems inhabiting a wide range of aquatic habitats. This study provides a first comprehensive DNA barcode library for the water mites of Montenegro. DNA barcodes were analysed from 233 specimens of water mites morphologically assigned to 86 species from 28 genera and 15 families. In the course of the study, four species, i.e. Lebertia reticulata (Koenike, 1919), Atractides inflatipalpis K.Viets, 1950, A. latipes (Szalay, 1935) and Parabrachypoda montii (Maglio, 1924) were molecularly confirmed as new for Montenegro and three species, i.e. Protzia octopora Lundblad, 1954, Piona laminata (Thor, 1901) and Unionicola ypsilophora (Bonz, 1783) are new for the Balkan Peninsula. Results are analysed using the Barcode Index Number system (BIN) and the Refined Single Linkage (RESL) of BOLD. The BIN assigned sequences to 98 clusters, while the RESL reveal 103 operational taxonomic units (OTUs). Unique BINs were revealed for 72 species (83.7%), whereas twelve species (14%) were characterised by two BINs and two species (2.3%) with three BINs. Amongst the studied taxa, 14 species were found with a high intraspecific sequence divergences (˃ 2.2%), emphasising the need for additional comprehensive morphological and molecu­lar analysis of these species.

Molecular Analysis Reveals a High Diversity of Anopheline Mosquitoes in Yanomami Lands and the Pantanal Region of Brazil

Identifying the species of the subfamily Anophelinae that are Plasmodium vectors is important to vector and malaria control. Despite the increase in cases, vector mosquitoes remain poorly known in Brazilian indigenous communities. This study explores Anophelinae mosquito diversity in the following areas: (1) a Yanomami reserve in the northwestern Amazon Brazil biome and (2) the Pantanal biome in southwestern Brazil. This is carried out by analyzing cytochrome c oxidase (COI) gene data using Refined Single Linkage (RESL), Assemble Species by Automatic Partitioning (ASAP), and tree-based multi-rate Poisson tree processes (mPTP) as species delimitation approaches. A total of 216 specimens collected from the Yanomami and Pantanal regions were sequenced and combined with 547 reference sequences for species delimitation analyses. The mPTP analysis for all sequences resulted in the delimitation of 45 species groups, while the ASAP analysis provided the partition of 48 groups. RESL analysis resulted in 63 operational taxonomic units (OTUs). This study expands our scant knowledge of anopheline species in the Yanomami and Pantanal regions. At least 18 species of Anophelinae mosquitoes were found in these study areas. Additional studies are now required to determine the species that transmit Plasmodium spp. in these regions.

Using whole-genome sequencing to assess the diversity of Paenibacillus larvae within an outbreak and a beekeeping operation

The spore-forming bacterium Paenibacillus larvae is the causative agent of American foulbrood (AFB), a devastating disease of honeybees (Apis mellifera). In the present study, we used whole-genome sequencing (WGS) to investigate an extensive outbreak of AFB in northwestern Slovenia in 2019. A total of 59 P . larvae isolates underwent WGS, of which 40 originated from a single beekeeping operation, to assess the diversity of P. larvae within the beekeeping operation, apiary and colony. By applying a case-specific single-linkage threshold of 34 allele differences (AD), whole-genome multilocus sequence typing (wgMLST) identified two outbreak clusters represented by ERIC II-ST11 clones. All isolates from a single beekeeping operation fell within cluster 1 and the median pairwise AD between them was 10 (range=1–22). The median pairwise AD for apiaries of the same beekeeping operation ranged from 8 to 11 (min.=1, max.=22). For colonies of the same apiary and honey samples from these colonies, the median pairwise AD ranged from 8 to 14 (min.=1, max.=20). The maximum within-cluster distance was 33 pairwise AD for cluster 1 and 44 for cluster 2 isolates. The minimum distance between the outbreak-related and non-related isolates was 37 AD, confirming the importance of associated epidemiological data for delineating outbreak clusters. The observed transmission events could be explained by the activities of honeybees and beekeepers. The present study provides insight into the genetic diversity of P. larvae at different levels and thus provides information for future AFB surveillance.

Construction of the Core Collection of Catalpa fargesii f. duclouxii (Huangxinzimu) Based on Molecular Markers and Phenotypic Traits

To promote the conservation and utilization of Catalpa fargesii f. duclouxii (Huangxinzimu) germplasm resources, a total of 252 accessions were used to construct a preliminary core collection according to phenotypic traits and single nucleotide polymorphism (SNP) markers. In this study, 24 phenotypic traits, namely, 9 quantitative traits and 15 qualitative traits, were investigated. The core collection of C. fargesii f. duclouxii (Huangxinzimu) was constructed to remove redundant samples from the collected materials. First, the phenotypic core collection, with a sample proportion of 30, consisting of 24 clones, was constructed according to two genetic distances (Euclidean distance and Mahalanobis), four system clustering methods (the unweighted pair-group average method, Ward’s method, the complete linkage method, and the single linkage method), and three sampling methods (random sampling, deviation sampling, and preferred sampling). The best construction strategies were selected for further comparison. Three core collections (D2C3S3-30, D2C3S3-50, and D2C3S3-70) were constructed according to the optimal construction strategy at three sampling proportions. The core collection D2C3S3-30 with the best parameters was evaluated by using six parameters: the mean difference percentage (MD), variance difference percentage (VD), periodic rate of range (CR), changeable rate of the coefficient of variation (VR), minimum rate of change (CRMIN), and maximum rate of change (CRMAX). Three core collections (M-30, M-50, and M-70) were constructed by molecular markers, and the optimal core collection M-30 was selected by using five parameters, namely, Ho, He, PIC, MAF, and loci. The combination of D2C3S3-30 and M-30 was used to construct the final core collection DM-45, 45 samples representing the complete range of phenotypic and genetic variability. In this study, phenotypic traits combined with molecular markers were used to construct core collections to effectively capture the entire range of trait variation, effectively representing the original germplasm and providing a basis for the conservation and utilization of C. fargesii f. duclouxii (Huangxinzimu).

Performance and character contributions to variability in okra (Abelmoschus esculentus L. Moench) genotypes

Okra is an important vegetable crop, but its optimal production is constrained by a myriad of problems including pests, poor agronomic practices, and improper varietal identification among others. A study was carried out to determine the field performance and contribution of agronomic characters to overall variation in eighteen okra genotypes over two locations, Teaching and Research Farms of the Federal University of Agriculture, Abeokuta, Nigeria and Rehoboth Farms Limited, Moniya, Ibadan, Nigeria. The experiments were laid down in a randomized complete block design with three replicates and data were collected on number of days to emergence, number of days to 50% flowering, number of branches per plant, pod length, pod width, number of pods per plant, plant height, pod weight, number of seeds per pod, number of ridges per pod, 100 seed weight, seed, and pod yield. The data were subjected to analysis of variance, heritability in the broad sense, principal component analysis as well as the single linkage cluster analysis. Results revealed significant (p <0.05) variation in the genotypes and high heritability estimates for most of the characters. Number of branches per plant, plant height, number of pods per plant accounted for the highest contributor to variations in the accessions while clustering analysis revealed genotypes; NGB00303, NGB00342 and NGB00346 were distant from all genotypes making them useful materials for hybridization studies.

Comparison of Single Linkage, Complete Linkage, and Average Linkage Methods on Community Welfare Analysis in Cities and Regencies in East Java

Community welfare is one of the important points for a region and is also the essence of national development. The welfare of the people in Indonesia is fairly unequal, especially in East Java. To be able to map an area to the welfare of its people in East Java, one way that can be used is to use clustering. The hierarchical clustering method is one of the clustering methods for grouping data. In hierarchical clustering, single linkage, complete linkage, and average linkage methods are suitable methods for grouping data, which will compare the best method to use. The results of the calculation show that the average linkage method with three clusters is the best calculation with a silhouette index value of 0.6054, with the 1st cluster there are 23 regions, namely the city/district with the highest community welfare, the 2nd cluster there are 11 regions, namely cities/districts with moderate social welfare, and in the third cluster there are 4 regions, namely cities/districts with the lowest community welfare.

Isometry Invariant Shape Recognition of Projectively Perturbed Point Clouds by the Mergegram Extending 0D Persistence

Rigid shapes should be naturally compared up to rigid motion or isometry, which preserves all inter-point distances. The same rigid shape can be often represented by noisy point clouds of different sizes. Hence, the isometry shape recognition problem requires methods that are independent of a cloud size. This paper studies stable-under-noise isometry invariants for the recognition problem stated in the harder form when given clouds can be related by affine or projective transformations. The first contribution is the stability proof for the invariant mergegram, which completely determines a single-linkage dendrogram in general position. The second contribution is the experimental demonstration that the mergegram outperforms other invariants in recognizing isometry classes of point clouds extracted from perturbed shapes in images.

Analisa Hasil Pengelompokan Wilayah Kejadian Non-Kebakaran Menggunakan Agglomerative Hierachical Clustering di Semarang

Abstrak−Klasterisasi merupakan metode pengelompokan dari data yang sudah diketahui label kelasnya untuk menemukan klaster baru dari hasil observasi. Dalam klasterisasi banyak metode yaitu metode terpusat, hirarki, kepadatan dan berbasis kisi, namun dalam penelitian yang dilakukan ini dipilih metode berbasis hirarki. Metode hirarki ini bekerja melakukan pengelompokan objek dengan membentuk hirarki klaster namun bukan berarti selalu digambarkan dengan hirarki dalam organsasi. Dipilihnya Agglomerative Hierarchical Clustering dimana merupakan jenis dari bawah ke atas atau biasa disebut (bottom-up) dalam metode ini objek yang akan diuji dianggap sebagai objek tunggal sebagai klaster dan lalu dilakukan iterasi untuk menemukan klaster-klaster yang lebih besar. Data yang akan digunakan adalah data non-kebakaran pada Dinas Pemadam Kebakaran Kota Semarang ynng mana akan dilakukan pengelompokan wilayah penanganan non-kebakaran. Dinas Pemadam Kebakaran melakukan penanganan bukan hanya kebakaran saja namun ada banyak hal yang sebenarnya dapat ditangani oleh petugas pemadam kebakaran, kejadian non-kebakaran ada beberapa seperti evakuasi reptil, evakuasi kucing, penyelamatan korban kecelakaan dan lain sebagainya. Dari data non-kebakaran dari 16 kecamatan di Kota Semarang pada tahun 2019 akan dilakukan uji menggunakan tiga algoritma yaitu Single Lingkage, Average Linkage dan Complete Linkage . Adapun dari algoritma Single Linkage dilakukan prosedur pemusatan dari jarak terkecil antar objek data, algoritma Average Linkage dilakukan prosedur dari jarak rata-rata objek data, sedangkan jika algoritma Complete Linkage dilakukan prosedur pemusatan dari jarak yang terbesar. Implementasi dan visualiasi dari data uji coba yang dilakukan di penilitian ini menggunakan tools WEKA 3.8.4, Wakaito Environment Analysis for Knowledge atau yang biasa dikenal dengan WEKA ini merupakan software yang menggunakan bahasa pemrograman java. Dari dataset 380 data diambil sampel 100 data untuk diuji mengunakan WEKA menggunakan metode perhtungan jarak Manhattan Distance dengan 3 cluster. Hasil dari data uji coba dapat divisualisasikan dengan visualisasi dendogram pada fitur visualize tree dan jika dilakukan visualisasi dalam bentuk grafik dapat dilakukan menggunakan fitur visualize clusters assignment.

A dual barcoding approach to bacterial strain nomenclature: Genomic taxonomy of Klebsiella pneumoniae strains

Sublineages within microbial species can differ widely in their ecology and pathogenicity, and their precise definition is important in basic research and industrial or public health applications. Whereas the classification and naming of prokaryotes is unified at the species level and higher taxonomic ranks, universally accepted definitions of sublineages within species are largely missing, which introduces confusion in population biology and epidemiological surveillance. Here we propose a broadly applicable genomic classification and nomenclature approach for bacterial strains, using the prominent public health threat Klebsiella pneumoniae as a model. Based on a 629-gene core genome multilocus sequence typing (cgMLST) scheme, we devised a dual barcoding system that combines multilevel single linkage (MLSL) clustering and life identification numbers (LIN). Phylogenetic and clustering analyses of >7,000 genome sequences captured population structure discontinuities, which were used to guide the definition of 10 infra-specific genetic dissimilarity thresholds. The widely used 7-gene multilocus sequence typing (MLST) nomenclature was mapped onto MLSL sublineages (threshold: 190 allelic mismatches) and clonal group (threshold: 43) identifiers for backwards nomenclature compatibility. The taxonomy is publicly accessible through a community-curated platform (https://bigsdb.pasteur.fr/klebsiella), which also enables external users genomic sequences identification.