Examples of bioactive peptides derived from internal sequences of proteins are known for decades. The great majority of these findings appear to be fortuitous rather than the result of a deliberate and methodological-based enterprise. In the present work, we describe the identification and the biological activities of novel antimicrobial peptides unveiled as internal fragments of various plant proteins founded on our hypothesis-driven search strategy. All putative encrypted antimicrobial peptides were selected based upon their physicochemical properties that were iteratively selected by an in-house computer program named Kamal. The selected peptides were chemically synthesized and evaluated for their interaction with model membranes. Sixteen of these peptides showed antimicrobial activity against human and/or plant pathogens, some with a wide spectrum of activity presenting similar or superior inhibition efficacy when compared to classical antimicrobial peptides (AMPs). These original and previously unforeseen molecules constitute a broader and undisputable set of evidences produced by our group that illustrate how the intragenic concept is a workable reality and should be carefully explored not only for microbicidal agents but also for many other biological functions.

Partial virus genome sequence with high nucleotide identity to Cotton leafroll dwarf virus (CLRDV) was identified from two cotton (Gossypium hirsutum) samples from Thailand displaying typical cotton leaf roll disease symptoms. We developed and validated a PCR assay for the detection of CLRDV isolates from Thailand and Brazil.

Since 2006, Brazilian cotton (Gossypium hirsutum) crops planted with cultivars that are resistant to cotton blue disease have developed a new disease termed "atypical" cotton blue disease or atypical vein mosaic disease. Here, we describe the complete genomes of two virus isolates associated with this disease. The new virus isolates, called CLRDV-Acr3 and CLRDV-IMA2, were found to have a high degree of nucleotide and amino acid sequence similarity to previously described isolates of cotton leafroll dwarf virus, the causal agent of cotton blue disease. However, their P0 proteins were 86.1 % identical. These results show that this new disease is caused by a new CLRDV genotype that seems to have acquired the ability to overcome cotton blue disease resistance.

Genotypic and phenotypic variation among 16 isolates of Ramularia areola of Gossypium hirsutum collected from five different geographical regions of Brazil was studied through virulence spectrum on three cultivars in the glasshouse and through ERIC-and REP-PCR and ITS1-5.8S-ITS2 rDNA analysis. Difference in virulence spectrum and molecular analysis of some isolates was observed. ERIC-and REP-PCR showed similar results and revealed a high level of diversity among the isolates. A unique profile for both ERIC and REP was obtained for most isolates. On the other hand, the ITS rDNA analysis did not show different PCR-RFLP patterns. While some isolates differed among each other considering genotypic and phenotypic reactions, no clear cut evidence was found about the existence of genetic lineages of R. areola in Brazil. Identification of genetic variability among the R. areola isolates originated from different geographic regions would permit screening of Brazilian germplasm and achieve sources with a wide spectrum of resistance. This is the first report of the genotypic and phenotypic variability among the R. areola isolates originated from five cotton growing regions of Brazil.

The reduction of the production cost and negative environmental impacts by pesticide application to control cotton diseases depends on the infection patterns spatialized in the farm scale. Here, we evaluate the potential of three-band multispectral imagery from a multi-rotor unmanned airborne vehicle (UAV) platform for the detection of ramularia leaf blight from different flight heights in an experimental field. Increasing infection levels indicate the progressive degradation of the spectral vegetation signal, however, they were not sufficient to differentiate disease severity levels. At resolutions of ~5 cm (100 m) and ~15 cm (300 m) up to a ground spatial resolution of ~25 cm (500 m flight height), two-scaled infection levels can be detected for the best performing algorithm of four classifiers tested, with an overall accuracy of ~79% and a kappa index of ~0.51. Despite limited classification performance, the results show the potential interest of low-cost multispectral systems to monitor ramularia blight in cotton.