More years of field trials against Plasmopara viticola in organic viticolture

The organic viticulture assumes a decisive role in the national agricultural sector. More impelling problems in the management of organic vineyards are represented from the plants pathology defence and particularly of Plasmopara viticola containment. Copper represents one of the few usable fungicides in the organic farming and the only effective against downy mildew. With Regulation EC n. 473/2002, fixed maximum quantity usable of copper compounds, owing to the environment problems due to the copper accumulation in the soil. To reduce quantity of metal copper or replace it with natural products, are conducted field trials with copper compounds at a low rate or alternative to copper products. Besides, we are estimating possibility to reduce the operations against P. viticola optimizing fungicidal treatments. Field trials in the organic farms located near Rome, have been carried out. Guidelines EPPO/OEPP PP 1/31 (3) have been carried out. The results of the trials have showed that, using cupric products with low metallic content, to reduce copper quantities used, always allowed to respect the limits established by Regulation EC. The alternative products that were investigated have not guaranteed, instead, an adequate protection in high pressure of grapevine downy mildew. It was possible to reduce treatments against P. viticola through control of different environmental parameters. The trials confirm that the copper is indispensable for plant protection in organic farming as it is not possible to replace it with natural extracts substances. We can reduce, instead, the copper quantities used trough the use of new products with low quantity metal copper or through the evaluation of climatic and pedologic data that allow to rationalize the fungicidal treatments.     

Three Types of Sensitivity to Metalaxyl in Plasmopara viticola

The sensitivity of Plasmopara-isolates to metalaxyl was determined by a leaf disc test. For the first time this permitted the distinction and clear characterization of not only sensitive and resistant isolates but also of isolates belonging to a third type of sensitivity, termed the reduced sensitive type.     

Population genetic structure of Plasmopara viticola after 125 years of colonization in European vineyards

To examine the within- and among-population genetic structure of Plamopara viticola oosporic populations in Europe, 8991 lesions from 32 vineyard plots were collected and analysed. Four multi-allelic microsatellite markers were used to genotype the pathogen. All populations had high levels of gene and genotypic diversity. Most populations were in Hardy–Weinberg equilibrium and thus randomly mating. Among P. viticola populations, significant low to moderate genetic differentiation was observed, even between geographically close populations. This genetic differentiation was also evident in the neighbour-joining phylogenetic genetic distance tree, showing clear substructure and distinguishing mainly five clusters based on geographical origin. Significant isolation by distance was found in central European P. viticola populations, suggesting a step-wise migration model. No significant isolation by distance was found within Greek populations, most probably owing to natural geographical barriers such as the sea and mountains, as well as the frequent population bottlenecks occurring in these populations, preventing natural migration among populations. The high variability of P. viticola provides explanation for its successful infestation of the heterogeneous European vineyards in the last 125 years after its introduction.     

The use of artificial neural networks to predict the presence of small-bodied fish in a river

1. Discriminant factorial analysis (DFA) and artificial neural networks (ANN) were used to develop models of presence/absence for three species of small-bodied fish (minnow, Phoxinus phoxinus , gudgeon, Gobio gobio , and stone loach, Barbatula barbatula ).
2. Fish and ten environmental variables were sampled using point abundance sampling by electrofishing in the Ariège River (France) at 464 sampling points.
3. Using DFA, the percentage of correct assignments, expressed as the percentage of individuals correctly classified over the total number of examined individuals, was 62.5% for stone loach, 66.6% for gudgeon and 78% for minnow. With back-propagation of ANN, the recognition performance obtained after 500 iterations was: 82.1% for stone loach, 87.7% for gudgeon and 90.1% for minnow.
4. The better predictive performance of the artificial neural networks holds promise for other situations with non-linearly related variables.     

Subcellular localization and functional analyses of a PR10 protein gene from Vitis pseudoreticulata in response to Plasmopara viticola infection

Downy mildew, caused by the oomycete Plasmopara viticola, is a serious fungal disease in the cultivated European grapevines (Vitis vinifera L.). The class 10 of pathogenesis-related (PR) genes in grapevine leaves was reported to be accumulated at mRNA level in response to P. viticola infection. To elucidate the functional roles of PR10 genes during plant–pathogen interactions, a PR10 gene from a fungal-resistant accession of Chinese wild Vitis pseudoreticulata (designated VpPR10.2) was isolated and showed high homology to PR10.2 from susceptible V. vinifera (designated VvPR10.2). Comparative analysis displayed that there were significant differences in the patterns of gene expression between the PR10 genes from the two host species. VpPR10.2 was induced with high level in leaves infected by P. viticola, while VvPR10.2 showed a low response to this inoculation. Recombinant VpPR10.2 protein showed DNase activity against host genomic DNA and RNase activity against yeast total RNA in vitro. Meanwhile, recombinant VpPR10.2 protein inhibited the growth of tobacco fungus Alternaria alternata and over-expression of VpPR10.2 in susceptible V. vinifera enhanced the host resistance to P. viticola. The results from subcellular localization analysis showed that VpPR10.2 proteins were distributed dynamically inside or outside of host cell. Moreover, they were found in haustorium of P. viticola and nucleus of host cell which was associated with a nucleus collapse at 10 days post-inoculation. Taken together, these results suggested that VpPR10.2 might play an important role in host plant defense against P. viticola infection.     

Anti-glycated activity prediction of polysaccharides from two guava fruits using artificial neural networks

High-efficiency ultrasonic treatment was used to extract the polysaccharides of Psidium guajava (PPG) and Psidium littorale (PPL). The aims of this study were to compare polysaccharide activities from these two guavas, as well as to investigate the relationship between ultrasonic conditions and anti-glycated activity. A mathematical model of anti-glycated activity was constructed with the artificial neural network (ANN) toolbox of MATLAB software. Response surface plots showed the correlation between ultrasonic conditions and bioactivity. The optimal ultrasonic conditions of PPL for the highest anti-glycated activity were predicted to be 256 W, 60 °C, and 12 min, and the predicted activity was 42.2%. The predicted highest anti-glycated activity of PPG was 27.2% under its optimal predicted ultrasonic condition. The experimental result showed that PPG and PPL possessed anti-glycated and antioxidant activities, and those of PPL were greater. The experimental data also indicated that ANN had good prediction and optimization capability.     

A single point mutation in the novel PvCesA3 gene confers resistance to the carboxylic acid amide fungicide mandipropamid in Plasmopara viticola

The grapevine downy mildew, Plasmopara viticola, is one of the most devastating pathogens in viticulture. Effective control is mainly based on fungicide treatments, although resistance development in this pathogen is reported for a number of fungicides. In this study we describe for the first time the molecular mechanism of resistance to a carboxylic acid amide (CAA) fungicide. We identified a family of four cellulose synthase (CesA) genes containing conserved domains that are found in all processive glycosyltransferases. Phylogenetic analysis revealed their close relationship to the cellulose synthases of Phytophthora sp. Sequencing of the CesA genes in a CAA- resistant and -sensitive field isolate revealed five single nucleotide polymorphisms (SNPs) affecting the amino acid structure of the proteins. SNP inheritance in F₁-, F₂- and F₃-progeny confirmed resistance to be correlated with one single SNP located in PvCesA3. Only if present in both alleles, this SNP led to the substitution of a glycine for a serine residue at position 1105 (G1105S) in the deduced amino acid sequence, thus conferring CAA- resistance. Our data demonstrate that the identified genes are putative cellulose synthases and that one recessive mutation in PvCesA3 causes inheritable resistance to the CAA fungicide mandipropamid.     

Characterization of ecosystem responses to climatic controls using artificial neural networks

Understanding and modeling ecosystem responses to their climatic controls is one of the major challenges for predicting the effects of global change. Usually, the responses are implemented in models as parameterized functional relationships of a fixed type. In contrast, the inductive approach presented here based on artificial neural networks (ANNs) allows the relationships to be extracted directly from the data. It has been developed to explore large, fragmentary, noisy, and multidimensional datasets, such as the carbon fluxes measured at the ecosystem level with the eddy covariance technique. To illustrate this, our approach has been systematically applied to the daytime carbon flux dataset of the deciduous broadleaf forest Hainich in Germany. The total explainable variability of the half‐hourly carbon fluxes from the driving climatic variables was 93.1%, showing the excellent data mining capability of the ANNs. Total photosynthetic photon flux density was identified as the dominant control of the daytime response, followed by the diffuse radiation. The vapor pressure deficit was the most important nonradiative control. From the ANNs, we were also able to deduce and visualize the dependencies and sensitivities of the response to its climatic controls. With respect to diffuse radiation, the daytime carbon response showed no saturation and the light use efficiency was three times greater for diffuse compared with direct radiation. However, with less potential radiation reaching the forest, the overall effect of diffuse radiation was slightly negative. The optimum uptake of carbon occurred at diffuse fractions between 30% and 40%. By identifying the hierarchy of the climatic controls of the ecosystem response as well as their multidimensional functional relationships, our inductive approach offers a direct interface to the data. This provides instant insight in the underlying ecosystem physiology and links the observational relationships to their representation in the modeling world.     

Europe as a bridgehead in the worldwide invasion history of grapevine downy mildew,Plasmopara viticola

1. Download : Download high-res image (258KB)
2. Download : Download full-size image

     

Application of artificial neural networks to modelling of starch hydrolysis by glucoamylase

The applicability of neural networks to the dynamic modelling of starch hydrolysis by Aspergillus niger glucoamylase is studied. The advantage of this technique is the possibility of predicting the reaction curves without a detailed kinetic model. Two independent neural models were proposed to predict the concentration of the products and conversion degree of the substrate at the end of the reaction (Model 1) as well as the reaction courses in the first stage when the sharp changes in the reaction rate are observed (Model 2). The results of simulations prove the ability of neural-network models to describe the complex kinetics of starch hydrolysis by glucoamylase.     

In vivo localization at the cellular level of stilbene fluorescence induced by Plasmopara viticola in grapevine leaves

Accurate localization of phytoalexins is a key for better understanding their role. This work aims to localize stilbenes, the main phytoalexins of grapevine. The cellular localization of stilbene fluorescence induced by Plasmopara viticola, the agent of downy mildew, was determined in grapevine leaves of very susceptible, susceptible, and partially resistant genotypes during infection. Laser scanning confocal microscopy and microspectrofluorimetry were used to acquire UV-excited autofluorescence three-dimensional images and spectra of grapevine leaves 5-6 days after inoculation. This noninvasive technique of investigation in vivo was completed with in vitro spectrofluorimetric studies on pure stilbenes as their fluorescence is largely affected by the physicochemical environment in various leaf compartments. Viscosity was the major physicochemical factor influencing stilbene fluorescence intensity, modifying fluorescence yield by more than two orders of magnitude. Striking differences in the localization of stilbene fluorescence induced by P. viticola were observed between the different genotypes. All inoculated genotypes displayed stilbene fluorescence in cell walls of guard cells and periclinal cell walls of epidermal cells. Higher fluorescence intensity was observed in guard-cell walls than in any other compartment due to increased local viscosity. In addition stilbene fluorescence was found in epidermal cell vacuoles of the susceptible genotype and in the infected spongy parenchyma of the partially resistant genotype. The very susceptible genotype was devoid of fluorescence both in the epidermal vacuoles and the mesophyll. This strongly suggests that the resistance of grapevine leaves to P. viticola is correlated with the pattern of localization of induced stilbenes in host tissues.     

Recent advances in the MOBJ algorithm for training artificial neural networks

This paper presents a new scheme for training MLPs which employs a relaxation method for multi-objective optimization. The algorithm works by obtaining a reduced set of solutions, from which the one with the best generalization is selected. This approach allows balancing between the training error and norm of network weight vectors, which are the two objective functions of the multi-objective optimization problem. The method is applied to classification and regression problems and compared with Weight Decay (WD), Support Vector Machines (SVMs) and standard Backpropagation (BP). It is shown that the systematic procedure for training proposed results on good generalization neural models, and outperforms traditional methods.     

A study of the application of Kohonen-type neural networks to the Travelling Salesman Problem

It is observed that animals often have to resolve difficult tasks of optimization and that this process can be studied by applying the formal framework of neural networks to a simple problem such as the Travelling Salesman Problem. Existing work is reviewed with particular emphasis on recent studies using self-organizing networks. An algorithm is described in which general principles developed by Kohonen are applied to the Travelling Salesman Problem. Simulation results are given for problem sets of up to 10,000 cities. The routes generated are reported as being slightly longer than those produced by simulating annealing; compute time is lower and scales less than quadratically with problem size. It is suggested that the ability to perform optimization is an emergent computational property not just of the Kohonen model but of any mechanism capable of producing topology-preserving mappings, including mechanisms within the brain.