Search

Določevanje odpornosti rastlin na povzročitelje gospodarsko pomembnih bolezni kot so npr. uvelost, rja in ožig, pomeni velik napredek za uspešno kmetijsko pridelavo. Identifikacija odpornih rastlin je danes hitrejša in bolj zanesljiva. Uporaba biotehnoloških pristopov, predvsem molekularnih markerjev na osnovi PCR, omogoča analizo genoma in posledično prisotnost genov za odpornost na določeno bolezen. Markerji, ki ločujejo med posameznimi genotipi na podlagi polimorfizmov služijo kot indikatorji prisotnosti gena za odpornost, če so v njegovi neposredni bližini. Odpornost leče (Lens culinaris Medik.) na uvelost je pogojena s Fw genom. V njegovi bližini 10,8 cM oddaljen, je RAPD marker OP-K15900. Tesnejšo povezavo so odkrili preko SSR in AFLP markerjev SSR59-2B je od Fw gena oddaljen 8,0 cM, p17m30710 pa 3,5 cM. Odpornost na rjo je determinirana z genom R, katerega prisotnost najbolje določata dva SSR markerja, LcSSR606 in LcSSR440. Njuna oddaljenost od R gena je 8,3 cM in 8,1 cM. Odpornost na uvelosti in rjo je determinirana z dominantnim genom. Ožig, ki je prav tako kot uvelost in rja posledica okužbe z glivo, nima enotnega vira odpornosti. Do zdaj je bil odkrit dominanten gen AbR1, kateremu sta najbližje RAPD markerja RB18 in RV01, ki sta oddaljena 14 cM oz. 6 cM. Poleg AbR1 je dominanten gen za odpornost tudi Ral2, recesivno pogojen pa je ral2. Crops resistant to economically important diseases such as fusarium wilt, rust and Ascochyta blight are essential for agricultural production. Identification of resistant plants is now faster and more reliable. The use of biotechnological methods, especially molecular markers based on PCR, enables the analysis of the genome and thus the presence of genes for disease resistance. Markers that differentiate between genotypes based on DNA polymorphisms serve as indicators of a resistance gene when located in its immediate vicinity. Wilt resistance in lentil (Lens culinaris Medik.) is caused by the Fw gene. The latter has a RAPD marker OP-K158900 in the proximity of 10,8 cM. A stronger association was detected with SSR marker SSR59-2B and AFLP marker p17m30710 at distances of 8,0 and 3,5 cM, respectively. Rust resistance is regulated by the R gene, which is flanked by two SSR markers LcSSR606 and LcSSR440 at distances 8,3 and 8,2 cM, respectively. Resistance to rust and wilt is determined by a dominant gene, in contrast to blight, which has two known dominant genes that determine resistance. Ascochyta blight has two RAPD markers, RB18 and RV01, near the AbR1 gene of 14 and 6 cM, respectively. Ral2 is another dominant gene, while ral2 is recessive.

Chestnut stands located in France, Italy, Hungary, Slovenia and Slovakia were surveyed for their phytosanitary situation. Chestnut blight is the most common disease in these woods. In the past, Cryphonectria parasitica has caused severe damage both in France and Italy but spontaneous re-growth of the blighted trees is occurring in almost all the visited stands. In Slovenia, Hungary and Slovakia the disease has been spreading recently. Healing and healed cankers and hypovirulent strains of the parasite are present in many of the investigated chestnut woods and a reduction of damage was related to their spread in all the investigated countries. Several intrusive species of trees are in competition with chestnut trees in abandoned stands. Ink disease, caused mainly by Phytophthora cambivora, was observed both in France and Italy stands and its damage appears very effective and increasing. The effects of both the diseases on the evolution and development of chestnut woods are evaluated and their sustainable management is discussed.

We investigate a framework for visual object categorization in artificial cognitive systems that is based on a discovery of appearance and structural similarities between object exemplars and prototypes that represent a category. Our main contribution is a novel approach for visual categorization of objects by synchronous hierarchical matching to a prototype, where high level matches between an object and a prototype are gradually discovered through several steps of binding, selection and inhibition. We show that categorization can be achieved without an excessive collection of evidence or learning from examples. Instead, by discovering commonalities between an object and a prototype, categorization can be based on the notion of ``family resemblances'' that does not require an explicit modeling of features to define a category. We combine the successful methods for categorization based on local features with a flexible and general prototype matching framework that balances the prevalent dependence of local methods on patch appearance by a gradual construction of high level features in a hierarchical feature binding process, where the descriptive power of features gradually increases from localized features with little or no structural information to complex composite features that span wider areas, until they encompass the whole object. We start with a low level description, which defines relatively stable local regions of interest (ROI) based on their appearance. Features are then dynamically constructed, or activated, in order to search for possible matches between the view being interpreted and a prototype view of an object. The composite features describe geometrical and photometric properties of a local area that expands, until a high level focused response, with a receptive field that potentially covers the whole object, is constructed. The construction of high level features is not steered by matching to a predefined set of features, but is rather conditioned by a hierarchical matching and inhibition of ad-hoc features. The advantage of such an approach is that we do not have to define a category with respect to a set of learning examples, since objects from the same category can be articulated using many different local geometrical and appearance matches. In order to characterize the appearance variation within local regions, we learn a codebook of ICA filters which impose a maximally sparse response; we intentionally keep the number of filters in the codebook low. In that way, local regions are strongly informed in terms of their structural (orientation, scale) properties, but carry only a basic information on appearance. Clusters of local features that conform to a subset of Gestalt rules are then grouped. This reduces the redundancy and provides a raw description of linear structures and equi-curvature areas. Based on a prototype image, we also learn the structure of the geometric conceptual space. The hierarchical binding is a massively parallel process that does not require global information. Twoplets of features are matched to twoplets of features, where the shape of the receptive field is conditioned to simulate attentive processing. The result is a set of matches of composite features at level the highest level of the hierarchy, where each of the features can be tracked to the constituent features at different levels. The decision whether the object categories match can be done based on the number of high level matches. We evaluate our framework on two domains: the first set of experiments assesses the performance of the hierarchical categorization on segmented images of objects that come from multiple categories, and the task is to find the closest match to a given image. The second set of experiments assesses the performance of hierarchical categorization of objects in occluded images. We use the ETH80 and the Caltech-101 public databases. The results show that our framework achieves a reliable categorization of objects in a canonical view. Besides categorization, it enables a reliable detection of an object as a whole, and of matching the object parts at different resolutions. We argue that the model could be integrated in a cognitive framework, where several other sources of information can be used to establish a stable interpretation of the scene. We also claim that categorization by visual matching to a prototype has biological plausibility, and that our architecture implements some ideas that originate in enactive cognitive science.