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BACKGROUND Expansins are proteins that loosen plant cell walls in a pH-dependent manner, probably by increasing the relative movement among polymers thus causing irreversible expansion. The expansin superfamily (EXP) comprises four distinct families: expansin A (EXPA), expansin B (EXPB), expansin-like A (EXLA) and expansin-like B (EXLB). There is(More)
Plants such as grapevine (Vitis spp.) display significant inter-cultivar genetic and phenotypic variation. The genetic components underlying phenotypic diversity in grapevine must be understood in order to disentangle genetic and environmental factors. We have shown that cDNA sequencing by RNA-seq is a robust approach for the characterization of varietal(More)
Grapevine berries undergo complex biochemical changes during fruit maturation, many of which are dependent upon the variety and its environment. In order to elucidate the varietal dependent developmental regulation of primary and specialized metabolism, berry skins of Cabernet Sauvignon and Shiraz were subjected to gas chromatography–mass spectrometry(More)
Lesion mimic mutants (LMMs) are a class of mutants in which hypersensitive cell death and defence responses are constitutively activated in the absence of pathogen attack. Various signalling molecules, such as salicylic acid (SA), reactive oxygen species (ROS), nitric oxide (NO), Ca(2+), ethylene, and jasmonate, are involved in the regulation of multiple(More)
Nowadays, large volumes of data and measurements are being continuously generated by computer and telecommunication networks, but such volumes make it difficult to extract meaningful knowledge from them. This paper presents SaFe-NeC, an innovative methodology for analyzing network traffic by exploiting data mining techniques, i.e. clustering and(More)
In this work, we attempt an exploratory analysis of spatio-temporal patterns of crime in San Francisco. We apply spectral analysis to the temporal evolution of all categories of crime, finding that many have a weekly or monthly periodicity, along with other components. We show that spatial distribution has weekly patterns as well. These results can improve(More)
Understanding the behavior of a network from a large scale traffic dataset is a challenging problem. Big data frameworks offer scalable algorithms to extract information from raw data, but often require a sophisticated fine-tuning and a detailed knowledge of machine learning algorithms. To streamline this process, we propose self-learning insightful network(More)
Sensing the perception of citizens on urban security is a key point in Smart City management. To address non-emergency issues municipalities commonly acquire citizens' reports and then analyze them offline to perform targeted actions. However, since non-emergency data potentially scale towards Big Data there is a need for open standards and technologies to(More)
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