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The RNA subunit of telomerase is an essential component whose primary sequence and length are poorly conserved among eukaryotic organisms. The phytopathogen Ustilago maydis is a dimorphic fungus of the order Ustilaginales. We analyzed several species of Ustilaginales to computationally identify the TElomere RNA (TER) gene ter1. To confirm the identity of the TER gene, we disrupted the gene and characterized telomerase-negative mutants. Similar to catalytic TERT mutants, ter1Δ mutants exhibit phenotypes of growth delay, telomere shortening and low replicative potential. ter1-disrupted mutants were unable to infect maize seedlings in heterozygous crosses and showed defects such as cell cycle arrest and segregation failure. We concluded that ter1, which encodes the TER subunit of the telomerase of U. maydis, have similar and perhaps more extensive functions than trt1.

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In recent decades, self-driving has been a topic of wide interest for Artificial Intelligence and the Automotive Industry. The irregularities detection on road surfaces is a task with great challenges. In developing countries, it is very common to find un-marked speed bumps on road surfaces which reduce the security and stability of self-driving cars. The existing techniques have not completely solved the speed bump detection without a well-marked signaling. The main contribution of this work is the design of a methodology that use a pre-trained convolutional neural network and supervised automatic classification, by using the analysis of elevations on surfaces through stereo vision, for detect well-marked and no well-marked speed bumps to improve existing techniques.