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.     

Histological study of the responses of two Vitis vinifera cultivars (resistant and susceptible) to Plasmopara viticola infections

Leaves of Vitis vinifera L. cvs. Chasselas (susceptible) and Solaris (resistant) were inoculated with Plasmopara viticola. Samples were then examined by scanning electron microscopy, light and epifluorescence microscopy. On the resistant cv. Solaris, stomatal deposits, identified as callose, were visible around the germinating zoospores 7 h after inoculation. Twenty-four hours after inoculation, infected stomata exhibited secretions that enveloped the zoospores. At this time, infected stomata were surrounded by necrotic tissues. At 120 h after inoculation, undefined material was deposited on the cuticle in the necrotic areas. Stomata in the vicinity of the infection sites contained callose deposits in and around the stomatal openings, but no necrotic zones were observed. On the sensitive cv. Chasselas neither secretion nor callose deposits were observed. Sporangiophores emerged about 96 h after inoculation and were fully developed 24 h later. Sporulation through small stomata-like apertures present all along the primary vein was also observed on the upper leaf surface. The role of stomatal callose deposits in the defense reactions of the Solaris grapevine cultivar against P. viticola is discussed.     

葡萄感霜霉病基因的分子标记(英文)

 在葡萄抗病育种中 ,幼苗期排除感霜霉病的后代具有特别重要的意义 .用 BSA,RAPD和SCAR方法研究了葡萄感霜霉病基因的分子标记 .分析了两个种间杂交组合 [毛葡萄 (抗病 )×欧洲葡萄 (感病 ) ]88- 1 1 0和 88- 84与 88- 1 1 0的 F1代自交或互交所得的 3个 F2 代 ,以及欧洲葡萄品种和中国野生葡萄种 .共筛选了 2 80个随机引物 .引物 OPO1 0产生了一个 RAPD标记 OPO1 0 - 80 0与葡萄感霜霉病主效基因紧密联锁 .将该 DNA片段克隆并测序 .OPO1 0 - 80 0的实际长度为 835bp,所以 OPO1 0 - 80 0应为 OPO1 0 - 835.据其两端序列 ,设计了一对长度为 2 6bp和 2 8bp的特异引物分别扩增上述试材 ,获得了与该 RAPD标记相同大小的一条带 ,将 RAPD标记转化为 SCAR标记SCO1 0 - 835.并证实了此 SCAR标记的通用性 ,该 SCAR标记可用于葡萄抗病育种中杂种后代对霜霉病的抗病与感病性鉴定 .     

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.     

A Plasmopara viticola RXLR effector targets a chloroplast protein PsbP to inhibit ROS production in grapevine

Pathogens secrete a large number of effectors that manipulate host processes to create an environment conducive to pathogen colonization. However, the underlying mechanisms by which Plasmopara viticola effectors manipulate host plant cells remain largely unclear. In this study, we reported that RXLR31154, a P. viticola RXLR effector, was highly expressed during the early stages of P. viticola infection. In our study, stable expression of RXLR31154 in grapevine (Vitis vinifera) and Nicotiana benthamiana promoted leaf colonization by P. viticola and Phytophthora capsici, respectively. By yeast two-hybrid screening, the 23-kDa oxygen-evolving enhancer 2 (VpOEE2 or VpPsbP), encoded by the PsbP gene, in Vitis piasezkii accession Liuba-8 was identified as a host target of RXLR31154. Overexpression of VpPsbP enhanced susceptibility to P. viticola in grapevine and P. capsici in N. benthamiana, and silencing of NbPsbPs, the homologs of PsbP in N. benthamiana, reduced P. capcisi colonization, indicating that PsbP is a susceptibility factor. RXLR31154 and VpPsbP protein were co-localized in the chloroplast. Moreover, VpPsbP reduced H₂O₂ accumulation and activated the ¹O₂ signaling pathway in grapevine. RXLR31154 could stabilize PsbP. Together, our data revealed that RXLR31154 reduces H₂O₂ accumulation and activates the ¹O₂ signaling pathway through stabilizing PsbP, thereby promoting disease.     

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.     

Influence of constitutive phenolic compounds on the response of grapevine (Vitis vinifera L.) leaves to infection by Plasmopara viticola

Flavonols and hydroxycinnamic acids are known to contribute to plant resistance against pathogens, but there are few reports on the implication of flavonols in the resistance of grapevine against Plasmopara viticola, and none on the involvement of hydroxycinnamic acids. In order to analyze the effect of flavonols on P. viticola infection, variable amounts of flavonols were induced by different light conditions in otherwise phenologically identical leaves. Differences in content of leaf hydroxycinnamic acids were induced at the same time. A non-invasive monitoring of flavonols and hydroxycinnamic acids was performed with Dualex leaf-clip optical sensors. Whatever the light condition, there were no significant changes in flavonol or in hydroxycinnamic acid contents for control and inoculated leaves during the development of P. viticola until 6 days after inoculation. The violet-blue autofluorescence of stilbenes, the main phytoalexins of grapevine that accumulate in inoculated leaves, was used as an indicator of infection by P. viticola. The implication of leaf constitutive flavonols and hydroxycinnamic acids in the defence of Vitis vinifera against P. viticola could be investigated in vivo thanks to this indicator. The increase in stilbene violet-blue autofluorescence started earlier for leaves with low flavonol content than for leaves with higher content, suggesting that constitutive flavonols are able to slow down the infection by P. viticola. On the contrary, constitutive hydroxycinnamic acids did not seem to play a role in defence against P. viticola. The non-destructive nature of the methods used alleviates the major problem of destructive experiments: the large variability in leaf phenolic contents.     

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.     

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

To facilitate decision support in freshwater ecosystem protection and restoration management, habitat suitability models can be very valuable. Data driven methods such as artificial neural networks (ANNs) are particularly useful in this context, seen their time-efficient development and relatively high reliability. However, specialized and technical literature on neural network modelling offers a variety of model development criteria to select model architecture, training procedure, etc. This may lead to confusion among ecosystem modellers and managers regarding the optimal training and validation methodology. This paper focuses on the analysis of ANN development and application for predicting macroinvertebrate communities, a species group commonly used in freshwater assessment worldwide. This review reflects on the different aspects regarding model development and application based on a selection of 26 papers reporting the use of ANN models for the prediction of macroinvertebrates. This analysis revealed that the applied model training and validation methodologies can often be improved and moreover crucial steps in the modelling process are often poorly documented. Therefore, suggestions to improve model development, assessment and application in ecological river management are presented. In particular, data pre-processing determines to a high extent the reliability of the induced models and their predictive relevance. This also counts for the validation criteria, that need to be better tuned to the practical simulation requirements. Moreover, the use of sensitivity methods can help to extract knowledge on the habitat preference of species and allow peer-review by ecological experts. The selection of relevant input variables remains a critical challenge as well. Model coupling is a missing crucial step to link human activities, hydrology, physical habitat conditions, water quality and ecosystem status. This last aspect is probably the most valuable aspect to enable decision support in water management based on ANN models.