A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.

There was a significant difference in the microbial community composition between diabetic patients and healthy subjects. A high abundance of Acinetobacter in the OS of diabetic patients may arise from the unique characteristics of the OS compared with those of other organ surfaces.

Soybean rust, caused by Phakopsora pachyrhizi, is a destructive foliar disease in nearly all soybean-producing countries. To identify genes controlling resistance to soybean rust, transcriptome profiling was conducted in resistant and susceptible Glycine tomentella genotypes triggered by P. pachyrhizi infection. Among 38,400 genes monitored using a soybean microarray, at 5% false discovery rate, 1,342 genes were identified exhibiting significant differential expression between uninfected and P. pachyrhizi-infected leaves at 12, 24, 48, and 72 h post-inoculation (hpi) in both rust-susceptible and rust-resistant genotypes. Differentially expressed genes were grouped into 12 functional categories, and among those, large numbers relate to basic plant metabolism. Transcripts for genes involved in the phenylpropanoid pathway were up-regulated early during rust infection. Similarly, genes coding for proteins related to stress and defense responses such as glutathione-S-transferases, peroxidases, heat shock proteins, and lipoxygenases were consistently up-regulated following infection at all four time points. Whereas, subsets of genes involved in cellular transport, cellular communication, cell cycle, and DNA processing were down-regulated. Quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) on randomly selected genes from the different categories confirmed these findings. Of differentially expressed genes, those associated with the flavonoid biosynthesis pathway as well as those coding for peroxidases and lipoxygenases were likely to be involved in rust resistance in soybean, and would serve as good candidates for functional studies. These findings provided insights into mechanisms underlying resistance and general activation of plant defense pathways in response to rust infection.

Glycine latifolia (Benth.) Newell & Hymowitz (2n = 40), one of the 27 wild perennial relatives of soybean, possesses genetic diversity and agronomically favorable traits that are lacking in soybean. Here, we report the 939-Mb draft genome assembly of G. latifolia (PI 559298) using exclusively linked-reads sequenced from a single Chromium library. We organized scaffolds into 20 chromosome-scale pseudomolecules utilizing two genetic maps and the Glycine max (L.) Merr. genome sequence. High copy numbers of putative 91-bp centromere-specific tandem repeats were observed in consecutive blocks within predicted pericentromeric regions on several pseudomolecules. No 92-bp putative centromeric repeats, which are abundant in G. max, were detected in G. latifolia or Glycine tomentella. Annotation of the assembled genome and subsequent filtering yielded a high confidence gene set of 54 475 protein-coding loci. In comparative analysis with five legume species, genes related to defense responses were significantly overrepresented in Glycine-specific orthologous gene families. A total of 304 putative nucleotide-binding site (NBS)-leucine-rich-repeat (LRR) genes were identified in this genome assembly. Different from other legume species, we observed a scarcity of TIR-NBS-LRR genes in G. latifolia. The G. latifolia genome was also predicted to contain genes encoding 367 LRR-receptor-like kinases, a family of proteins involved in basal defense responses and responses to abiotic stress. The genome sequence and annotation of G. latifolia provides a valuable source of alternative alleles and novel genes to facilitate soybean improvement. This study also highlights the efficacy and cost-effectiveness of the application of Chromium linked-reads in diploid plant genome de novo assembly.