30 research outputs found

    Radio Di Kawasan Perbatasan Indonesia Dalam Centering the Margin

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    Kawasan perbatasan Indonesia banyak mengalami blank spot layanan informasi sehingga siaran yang menghubungkan warganegara dan pemerintah tidak tersampaikan dengan baik. Padahal, keberadaan media di perbatasan sangat strategis sebagai penyedia informasi yang merefl eksikan dinamika lokal, mengartikulasikan kepentingan daerah sehingga dapat didengar oleh pusat. Harapannya, artikulasi tersebut dapat memberi warna pada dinamika sosial, politik, ekonomi, dan budaya di tanah air. Tulisan ini mengeksplorasi bagaimana radio di wilayah perbatasan memberikan kontribusi dalam peran centering the margin, yakni membawa aspirasi di perbatasan guna “memusatkan yang pinggir”

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    A description of the results of the cross platform (385 K CGH and SNP50 chip) verification of CNV regions [72, 73]. (PDF 8 kb

    MOESM3 of A multi-trait Bayesian method for mapping QTL and genomic prediction

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    Additional file 3: Table S2. Posterior mean number of SNPs in each distribution [0, 0.0001, 0.001 or 0.01 of the pedigree estimated genetic variance], data from Kemper et al. [1]

    MOESM1 of A multi-trait Bayesian method for mapping QTL and genomic prediction

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    Additional file 1: Table S1. Phenotypic and genetic correlation between milk yield traits, with trait heritability from the pedigree-based multi-trait model

    MOESM5 of A multi-trait Bayesian method for mapping QTL and genomic prediction

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    Additional file 5: Table S4. This file contains highly significant SNPs (P < 1 × 10−6) from the mixed model analysis of the milk minerals and proteins

    MOESM4 of A multi-trait Bayesian method for mapping QTL and genomic prediction

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    Additional file 4: Table S3. Top 100 SNPs with the highest mean posterior probability (PP) for inclusion in the model from the Holstein/Jersey reference population using BayesMV [39–41]

    MOESM7 of A multi-trait Bayesian method for mapping QTL and genomic prediction

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    Additional file 7: Table S5. Genomic prediction accuracy and bias from the univariate GBLUP model, data from Kemper et al. [1]

    MOESM8 of A multi-trait Bayesian method for mapping QTL and genomic prediction

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    Additional file 8: Figure S3. Genetic relationship between nine dairy and beef cattle breeds

    MOESM2 of A multi-trait Bayesian method for mapping QTL and genomic prediction

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    Additional file 2: Figure S1. Mean power and false-discovery rate for QTL discovery in simulated data for a single trait
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