Is there any genetic variation among native mexican and argentinian populations of Dalbulus maidis (Hemiptera: Cicadellidae)?.

The corn leafhopper Dalbulus maidis (Delong & Wolcott) (Hemiptera: Cicadellidae) originated in Mexico, but is found from southeastern and southwestern USA to Argentina. Differences in reproductive and phenotypic traits between Mexican (native) and Argentinian (adventive) populations have been previously reported, but information on their genetic variation is currently unavailable. The objective was to investigate possible genetic variability among D. maidis populations collected in Mexico on maize and maize relatives (annual and perennial teosintes) and on maize in Argentina. A region of the mitochondrial gene coding for the cytochrome oxidase subunit I (mtCOI) and the ribosomal internal transcribed spacer (ITS2) were sequenced and analyzed. We developed the forward and reverse primers for the DNA amplification of COI in D. maidis (dalCOI). Twenty two and 17 sequences for dalCOI and ITS2, respectively, were generated and analyzed. No genetic variation among Mexican and Argentinian populations was found in the ribosomal region and low genetic variation was found in the mitochondrial region. These results could be explained by the short evolutionary time scale, since both maize and the corn leafhopper moved throughout the Americas only in the most recent millenia, or in part to the limited host range, and thus a limited change in the corn leafhopper associated bacteria.Fil: Palomera, Veronica. Universidad de Guadalajara; MéxicoFil: Bertin, Sabrina. Universidad de Torino ; ItaliaFil: Rodriguez, Aaron. Universidad de Guadalajara; MéxicoFil: Bosco, Domenico. Universidad de Torino; ItaliaFil: Virla, Eduardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Planta Piloto de Procesos Industriales Microbiológicos; ArgentinaFil: Moya-Raygoza, Gustavo. Universidad de Guadalajara; Méxic

Neural networks as building blocks for the design of efficient learned indexes

The new area of Learned Data Structures consists of mixing Machine Learning techniques with those specific to Data Structures, with the purpose to achieve time/space gains in the performance of those latter. The perceived paradigm shift in computer architectures, that would favor the employment of graphics/tensor units over traditional central processing units, is one of the driving forces behind this new area. The advent of the corresponding branch-free programming paradigm would then favor the adoption of Neural Networks as the fundamental units of Classic Data Structures. This is the case of Learned Bloom Filters. The equally important field of Learned Indexes does not appear to make use of Neural Networks at all. In this paper, we offer a comparative experimental investigation regarding the potential uses of Neural Networks as a fundamental building block of Learned Indexes. Our results provide a solid and much-needed evaluation of the role Neural Networks can play in Learned Indexing. Based on our findings, we highlight the need for the creation of highly specialised Neural Networks customised to Learned Indexes. Because of the methodological significance of our findings and application of Learned Indexes in strategic domains, such as Computer Networks and Databases, care has been taken to make the presentation of our results accessible to the general audience of scientists and engineers working in Neural Networks and with no background about Learned Indexing

CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification

Background: Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data. Results: In this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel levels are devoted to catching both non-periodic and periodic DNA string features. A dense layer is devoted to their combination to give a final classification. Conclusions: Results computed on public data sets of different organisms show that CORENup is a state of the art methodology for nucleosome positioning identification based on a Deep Neural Network architecture. The comparisons have been carried out using two groups of datasets, currently adopted by the best performing methods, and CORENup has shown top performance both in terms of classification metrics and elapsed computation time