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  • Earth Resources and Remote Sensing  (1)
  • eEF-1α  (1)
  • Animals
  • Arabidopsis thaliana
  • 1990-1994  (2)
  • 1
    ISSN: 1573-5028
    Keywords: soybean seedlings ; eEF-1α ; tef genes ; cDNA ; genomic DNA ; dark/light transcription ; intron
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract A cDNA and a genomic DNA library from soybean (Glycine max L.) were used to identify and sequence two genes coding for the α-subunit of the translation elongation factor eEF-1. Within the coding part, the two genes (tefS1 andtefS2) diverge in 80 wobble positions thus yielding an identical protein composed of 447 amino acids. The soybean protein has about 95% similarity with eEF-1α proteins ofArabidopsis thaliana and tomato. Both genesS1 andS2 contain, within the coding part at a site seemingly unique to higher plants, a single short intron of 86 and 116 nucleotides, respectively. The untranslated leader part of both genes is interrupted by a large intron (partially sequenced). GenesS1 andS2 are transcribed in young leaves. cDNA and gene-specific oligonucleotide probes interact with a unique transcript of close to 1.9 kb. Northern hybridization studies using RNAs from dark- and light-grown seedlings show that light sharply increases the level of stable transcripts (1.9 kb). A peak value is measured after about 3 h of illumination, afterwards the transcript concentration drops to about 10% of the peak value. GenesS1 andS2 follow a similar transcription pattern in developing seedling leaves, which is distinct from that of therbcS genes measured in parallel experiments. According to northern results,S1 transcripts are more abundant in leaves at all measured stages of development thanS2 transcripts.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2019-07-10
    Description: This research note shows the results of applying a new massively parallel version of the automatic classification program (AutoClass IV) to a particular Landsat/TM image. The previous results for this image were produced using a "subsampling" technique because of the image size. The new massively parallel version of AutoClass allows the complete image to be classified without "subsampling", thus yielding improved results. The area in question is the FIFE study area in Kansas, and the classes AutoClass found show many interesting subtle variations in types of ground cover. Displays of the spatial distributions of these classes make up the bulk of this report. While the spatial distribution of some of these classes make their interpretation easy, most of the classes require detailed knowledge of the area for their full interpretation. We hope that some who receive this document can help us in understanding these classes. One of the motivations of this exercise was to test the new version of AutoClass (IV) that allows for correlation among the variables within a class. The scatter plots associated with the classes show that this correlation information is important in separating the classes. The fact that the spatial distribution of each of these classes is far from uniform, even though AutoClass was not given information about positions of pixels, shows that the classes are due to real differences in the image.
    Keywords: Earth Resources and Remote Sensing
    Type: FIA-94-01
    Format: text
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