RNA polymerase I promoter and splice acceptor site recognition affect gene expression in non-pathogenic Leishmania species

Leishmania (Sauroleishmania) tarentolae has biotechnological potential for use as live vaccine against visceral leishmaniasis and as a system for the over expression of eukaryotic proteins that possess accurate post-translational modifications. For both purposes, new systems for protein expression in this non-pathogenic protozoan are necessary. The ribosomal RNA promoter proved to be a stronger transcription driver since its use yielded increased levels of recombinant protein in organisms of both genera Trypanosoma or Leishmania. We have evaluated heterologous expression systems using vectors with two different polypyrimidine tracts in the splice acceptor site by measuring a reporter gene transcribed from L. tarentolae RNA polymerase I promoter. Our data indicate that the efficiency of chloramphenicol acetyl transferase expression changed drastically with homologous or heterologous sequences, depending on the polypyrimidine tract used in the construct and differences in size and/or distance from the AG dinucleotide. In relation to the promoter sequence the reporter expression was higher in heterologous lizard-infecting species than in the homologous L. tarentolae or in the mammalian-infecting L. (Leishmania) amazonensis.

Augmented reality systems for weed economic thresholds applications

The augmented reality (AR) technology has applications in many fields as diverse as aeronautics, tourism, medicine, and education. In this review are summarized the current status of AR and it is proposed a new application of it in weed science. The basic algorithmic elements for AR implementation are already available to develop applications in the area of weed economic thresholds. These include algorithms for image recognition to identify and quantify weeds by species and software for herbicide selection based on weed density. Likewise, all hardware necessary for AR implementation in weed science are available at an affordable price for the user. Thus, the authors propose weed science can take a leading role integrating AR systems into weed economic thresholds software, thus, providing better opportunities for science and computer-based weed control decisions.