Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) <doi:10.1177/08944393231178605>.
    Implements Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012) <doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing.

Imbibition causes seeds to expand, which results in the seed coat or testa being broken. Seed germination begins with imbibition. Imbibition aids in the transport of water into the developing ovules. Imbibition is required during the first stages of root water absorption.

On-target gene knockdown using siRNA ideally results from binding 
    fully complementary regions in mRNA transcripts to induce cleavage. 
    Off-target siRNA gene knockdown can occur through several modes, one being 
    a seed-mediated mechanism mimicking miRNA gene regulation. Seed-mediated 
    off-target effects occur when the ~8 nucleotides at the 5’ end of the guide 
    strand, called a seed region, bind the 3’ untranslated regions of mRNA, 
    causing reduced translation. Experiments using siRNA knockdown paired with 
    RNA-seq can be used to detect siRNA sequences with potential off-target 
    effects driven by the seed region. 'SeedMatchR' provides tools for exploring 
    and detecting potential seed-mediated off-target effects of siRNA in RNA-seq 
    experiments. 'SeedMatchR' is designed to extend current differential 
    expression analysis tools, such as 'DESeq2', by annotating results with 
    predicted seed matches. Using publicly available data, we demonstrate the 
    ability of 'SeedMatchR' to detect cumulative changes in differential gene 
    expression attributed to siRNA seed regions.

Analysis of seed germination data
    using the physiological time modelling approach. Includes functions
    to fit hydrotime and thermal-time models with the traditional approaches
    of Bradford (1990) <doi:10.1104/pp.94.2.840>
    and Garcia-Huidobro (1982) <doi:10.1093/jxb/33.2.288>. 
    Allows to fit models to grouped datasets,
    i.e. datasets containing multiple species, seedlots or experiments.