Detection and pattern recognition applied to leaves and chromosomes

The Project you are about to see it is based on the technologies used on object detection and recognition, especially on leaves and chromosomes. To do so, this document contains the typical parts of a scientific paper, as it is what it is. It is composed by an Abstract, an Introduction, points that have to do with the investigation area, future work, conclusions and references used for the elaboration of the document. The Abstract talks about what are we going to find in this paper, which is technologies employed on pattern detection and recognition for leaves and chromosomes and the jobs that are already made for cataloguing these objects. In the introduction detection and recognition meanings are explained. This is necessary as many papers get confused with these terms, specially the ones talking about chromosomes. Detecting an object is gathering the parts of the image that are useful and eliminating the useless parts. Summarizing, detection would be recognizing the objects borders. When talking about recognition, we are talking about the computers or the machines process, which says what kind of object we are handling. Afterwards we face a compilation of the most used technologies in object detection in general. There are two main groups on this category: Based on derivatives of images and based on ASIFT points. The ones that are based on derivatives of images have in common that convolving them with a previously created matrix does the treatment of them. This is done for detecting borders on the images, which are changes on the intensity of the pixels. Within these technologies we face two groups: Gradian based, which search for maximums and minimums on the pixels intensity as they only use the first derivative. The Laplacian based methods search for zeros on the pixels intensity as they use the second derivative. Depending on the level of details that we want to use on the final result, we will choose one option or the other, because, as its logic, if we used Gradian based methods, the computer will consume less resources and less time as there are less operations, but the quality will be worse. On the other hand, if we use the Laplacian based methods we will need more time and resources as they require more operations, but we will have a much better quality result. After explaining all the derivative based methods, we take a look on the different algorithms that are available for both groups. The other big group of technologies for object recognition is the one based on ASIFT points, which are based on 6 image parameters and compare them with another image taking under consideration these parameters. These methods disadvantage, for our future purposes, is that it is only valid for one single object. So if we are going to recognize two different leaves, even though if they refer to the same specie, we are not going to be able to recognize them with this method. It is important to mention these types of technologies as we are talking about recognition methods in general. At the end of the chapter we can see a comparison with pros and cons of all technologies that are employed. Firstly comparing them separately and then comparing them all together, based on our purposes. Recognition techniques, which are the next chapter, are not really vast as, even though there are general steps for doing object recognition, every single object that has to be recognized has its own method as the are different. This is why there is not a general method that we can specify on this chapter. We now move on into leaf detection techniques on computers. Now we will use the technique explained above based on the image derivatives. Next step will be to turn the leaf into several parameters. Depending on the document that you are referring to, there will be more or less parameters. Some papers recommend to divide the leaf into 3 main features (shape, dent and vein] and doing mathematical operations with them we can get up to 16 secondary features. Next proposition is dividing the leaf into 5 main features (Diameter, physiological length, physiological width, area and perimeter] and from those, extract 12 secondary features. This second alternative is the most used so it is the one that is going to be the reference. Following in to leaf recognition, we are based on a paper that provides a source code that, clicking on both leaf ends, it automatically tells to which specie belongs the leaf that we are trying to recognize. To do so, it only requires having a database. On the tests that have been made by the document, they assure us a 90.312% of accuracy over 320 total tests (32 plants on the database and 10 tests per specie]. Next chapter talks about chromosome detection, where we shall pass the metaphasis plate, where the chromosomes are disorganized, into the karyotype plate, which is the usual view of the 23 chromosomes ordered by number. There are two types of techniques to do this step: the skeletonization process and swiping angles. Skeletonization progress consists on suppressing the inside pixels of the chromosome to just stay with the silhouette. This method is really similar to the ones based on the derivatives of the image but the difference is that it doesnt detect the borders but the interior of the chromosome. Second technique consists of swiping angles from the beginning of the chromosome and, taking under consideration, that on a single chromosome we cannot have more than an X angle, it detects the various regions of the chromosomes. Once the karyotype plate is defined, we continue with chromosome recognition. To do so, there is a technique based on the banding that chromosomes have (grey scale bands] that make them unique. The program then detects the longitudinal axis of the chromosome and reconstructs the band profiles. Then the computer is able to recognize this chromosome. Concerning the future work, we generally have to independent techniques that dont reunite detection and recognition, so our main focus would be to prepare a program that gathers both techniques. On the leaf matter we have seen that, detection and recognition, have a link as both share the option of dividing the leaf into 5 main features. The work that would have to be done is to create an algorithm that linked both methods, as in the program, which recognizes leaves, it has to be clicked both leaf ends so it is not an automatic algorithm. On the chromosome side, we should create an algorithm that searches for the beginning of the chromosome and then start to swipe angles, to later give the parameters to the program that searches for the band profiles. Finally, on the summary, we explain why this type of investigation is needed, and that is because with global warming, lots of species (animals and plants] are beginning to extinguish. That is the reason why a big database, which gathers all the possible species, is needed. For recognizing animal species, we just only have to have the 23 chromosomes. While recognizing a plant, there are several ways of doing it, but the easiest way to input a computer is to scan the leaf of the plant. RESUMEN. El proyecto que se puede ver a continuación trata sobre las tecnologías empleadas en la detección y reconocimiento de objetos, especialmente de hojas y cromosomas. Para ello, este documento contiene las partes típicas de un paper de investigación, puesto que es de lo que se trata. Así, estará compuesto de Abstract, Introducción, diversos puntos que tengan que ver con el área a investigar, trabajo futuro, conclusiones y biografía utilizada para la realización del documento. Así, el Abstract nos cuenta qué vamos a poder encontrar en este paper, que no es ni más ni menos que las tecnologías empleadas en el reconocimiento y detección de patrones en hojas y cromosomas y qué trabajos hay existentes para catalogar a estos objetos. En la introducción se explican los conceptos de qué es la detección y qué es el reconocimiento. Esto es necesario ya que muchos papers científicos, especialmente los que hablan de cromosomas, confunden estos dos términos que no podían ser más sencillos. Por un lado tendríamos la detección del objeto, que sería simplemente coger las partes que nos interesasen de la imagen y eliminar aquellas partes que no nos fueran útiles para un futuro. Resumiendo, sería reconocer los bordes del objeto de estudio. Cuando hablamos de reconocimiento, estamos refiriéndonos al proceso que tiene el ordenador, o la máquina, para decir qué clase de objeto estamos tratando. Seguidamente nos encontramos con un recopilatorio de las tecnologías más utilizadas para la detección de objetos, en general. Aquí nos encontraríamos con dos grandes grupos de tecnologías: Las basadas en las derivadas de imágenes y las basadas en los puntos ASIFT. El grupo de tecnologías basadas en derivadas de imágenes tienen en común que hay que tratar a las imágenes mediante una convolución con una matriz creada previamente. Esto se hace para detectar bordes en las imágenes que son básicamente cambios en la intensidad de los píxeles. Dentro de estas tecnologías nos encontramos con dos grupos: Los basados en gradientes, los cuales buscan máximos y mínimos de intensidad en la imagen puesto que sólo utilizan la primera derivada; y los Laplacianos, los cuales buscan ceros en la intensidad de los píxeles puesto que estos utilizan la segunda derivada de la imagen. Dependiendo del nivel de detalles que queramos utilizar en el resultado final nos decantaremos por un método u otro puesto que, como es lógico, si utilizamos los basados en el gradiente habrá menos operaciones por lo que consumirá más tiempo y recursos pero por la contra tendremos menos calidad de imagen. Y al revés pasa con los Laplacianos, puesto que necesitan más operaciones y recursos pero tendrán un resultado final con mejor calidad. Después de explicar los tipos de operadores que hay, se hace un recorrido explicando los distintos tipos de algoritmos que hay en cada uno de los grupos. El otro gran grupo de tecnologías para el reconocimiento de objetos son los basados en puntos ASIFT, los cuales se basan en 6 parámetros de la imagen y la comparan con otra imagen teniendo en cuenta dichos parámetros. La desventaja de este método, para nuestros propósitos futuros, es que sólo es valido para un objeto en concreto. Por lo que si vamos a reconocer dos hojas diferentes, aunque sean de la misma especie, no vamos a poder reconocerlas mediante este método. Aún así es importante explicar este tipo de tecnologías puesto que estamos hablando de técnicas de reconocimiento en general. Al final del capítulo podremos ver una comparación con los pros y las contras de todas las tecnologías empleadas. Primeramente comparándolas de forma separada y, finalmente, compararemos todos los métodos existentes en base a nuestros propósitos. Las técnicas de reconocimiento, el siguiente apartado, no es muy extenso puesto que, aunque haya pasos generales para el reconocimiento de objetos, cada objeto a reconocer es distinto por lo que no hay un método específico que se pueda generalizar. Pasamos ahora a las técnicas de detección de hojas mediante ordenador. Aquí usaremos la técnica explicada previamente explicada basada en las derivadas de las imágenes. La continuación de este paso sería diseccionar la hoja en diversos parámetros. Dependiendo de la fuente a la que se consulte pueden haber más o menos parámetros. Unos documentos aconsejan dividir la morfología de la hoja en 3 parámetros principales (Forma, Dentina y ramificación] y derivando de dichos parámetros convertirlos a 16 parámetros secundarios. La otra propuesta es dividir la morfología de la hoja en 5 parámetros principales (Diámetro, longitud fisiológica, anchura fisiológica, área y perímetro] y de ahí extraer 12 parámetros secundarios. Esta segunda propuesta es la más utilizada de todas por lo que es la que se utilizará. Pasamos al reconocimiento de hojas, en la cual nos hemos basado en un documento que provee un código fuente que cucando en los dos extremos de la hoja automáticamente nos dice a qué especie pertenece la hoja que estamos intentando reconocer. Para ello sólo hay que formar una base de datos. En los test realizados por el citado documento, nos aseguran que tiene un índice de acierto del 90.312% en 320 test en total (32 plantas insertadas en la base de datos por 10 test que se han realizado por cada una de las especies]. El siguiente apartado trata de la detección de cromosomas, en el cual se debe de pasar de la célula metafásica, donde los cromosomas están desorganizados, al cariotipo, que es como solemos ver los 23 cromosomas de forma ordenada. Hay dos tipos de técnicas para realizar este paso: Por el proceso de esquelotonización y barriendo ángulos. El proceso de esqueletonización consiste en eliminar los píxeles del interior del cromosoma para quedarse con su silueta; Este proceso es similar a los métodos de derivación de los píxeles pero se diferencia en que no detecta bordes si no que detecta el interior de los cromosomas. La segunda técnica consiste en ir barriendo ángulos desde el principio del cromosoma y teniendo en cuenta que un cromosoma no puede doblarse más de X grados detecta las diversas regiones de los cromosomas. Una vez tengamos el cariotipo, se continua con el reconocimiento de cromosomas. Para ello existe una técnica basada en las bandas de blancos y negros que tienen los cromosomas y que son las que los hacen únicos. Para ello el programa detecta los ejes longitudinales del cromosoma y reconstruye los perfiles de las bandas que posee el cromosoma y que lo identifican como único. En cuanto al trabajo que se podría desempeñar en el futuro, tenemos por lo general dos técnicas independientes que no unen la detección con el reconocimiento por lo que se habría de preparar un programa que uniese estas dos técnicas. Respecto a las hojas hemos visto que ambos métodos, detección y reconocimiento, están vinculados debido a que ambos comparten la opinión de dividir las hojas en 5 parámetros principales. El trabajo que habría que realizar sería el de crear un algoritmo que conectase a ambos ya que en el programa de reconocimiento se debe clicar a los dos extremos de la hoja por lo que no es una tarea automática. En cuanto a los cromosomas, se debería de crear un algoritmo que busque el inicio del cromosoma y entonces empiece a barrer ángulos para después poder dárselo al programa que busca los perfiles de bandas de los cromosomas. Finalmente, en el resumen se explica el por qué hace falta este tipo de investigación, esto es que con el calentamiento global, muchas de las especies (tanto animales como plantas] se están empezando a extinguir. Es por ello que se necesitará una base de datos que contemple todas las posibles especies tanto del reino animal como del reino vegetal. Para reconocer a una especie animal, simplemente bastará con tener sus 23 cromosomas; mientras que para reconocer a una especie vegetal, existen diversas formas. Aunque la más sencilla de todas es contar con la hoja de la especie puesto que es el elemento más fácil de escanear e introducir en el ordenador.

Temperature and Photoperiod Effects on Vicia faba Phenology Simulated by CROPGRO-Fababean

Models may be useful tools to design efficient crop management practices provided they are able to accurately simulate the effect of weather variables on crop performance. The objective of this work was to accurately simulate the effects of temperature and day length on the rate of vegetative node expression, time to flowering, time to first pod, and time to physiological maturity of faba bean (Vicia faba L.) using the CROPGRO-Fababean model. Field experiments with multiple sowing dates were conducted in northwest Spain during 3 yr (17 sowing dates: 12 used for calibration and five for validation). Observed daily minimum and maximum air temperatures were within the range of ?9.0 and 39.2°C and observed photoperiods within 10.1 to 16.6 h. Optimization of thermal models to predict leaf appearance raised the base temperature (Tb) from the commonly used value of 0.0 to 3.9°C. In addition, photothermal models detected a small accelerating effect of day length on the rate of leaf appearance. Accurate prediction of the flowering date required incorporating day length, but the solved Tb approached negative values, close to ?4°C. All the reproductive phases after flowering were affected only by temperature, but postanthesis Tb was also mayor que0°C and approached values close to 8°C for time to first pod set and 5.5°C for time from first pod to physiological maturity. Our data indicated that cardinal base temperatures are not the same across all phenological phases.

The family of DOF transcription factors in Brachypodium distachyon: phylogenetic comparison with rice and barley DOFs and expression profiling

ABSTRACT: Transcription factors (TFs) are proteins that have played a central role both in evolution and in domestication, and are major regulators of development in living organisms. Plant genome sequences reveal that approximately 7% of all genes encode putative TFs. The DOF (DNA binding with One Finger) TF family has been associated with vital processes exclusive to higher plants and to their close ancestors (algae, mosses and ferns). These are seed maturation and germination, light-mediated regulation, phytohormone and plant responses to biotic and abiotic stresses, etc. In Hordeum vulgare and Oryza sativa, 26 and 30 different Dof genes, respectively, have been annotated. Brachypodium distachyon has been the first Pooideae grass to be sequenced and, due to its genomic, morphological and physiological characteristics, has emerged as the model system for temperate cereals, such as wheat and barley. RESULTS: Through searches in the B. distachyon genome, 27 Dof genes have been identified and a phylogenetic comparison with the Oryza sativa and the Hordeum vulgare DOFs has been performed. To explore the evolutionary relationship among these DOF proteins, a combined phylogenetic tree has been constructed with the Brachypodium DOFs and those from rice and barley. This phylogenetic analysis has classified the DOF proteins into four Major Cluster of Orthologous Groups (MCOGs). Using RT-qPCR analysis the expression profiles of the annotated BdDof genes across four organs (leaves, roots, spikes and seeds) has been investigated. These results have led to a classification of the BdDof genes into two groups, according to their expression levels. The genes highly or preferentially expressed in seeds have been subjected to a more detailed expression analysis (maturation, dry stage and germination). CONCLUSIONS: Comparison of the expression profiles of the Brachypodium Dof genes with the published functions of closely related DOF sequences from the cereal species considered here, deduced from the phylogenetic analysis, indicates that although the expression profile has been conserved in many of the putative orthologs, in some cases duplication followed by subsequent divergence may have occurred (neo-functionalization).

Disentangling factors controlling fruit and seed removal by rodents in temperate forests

Fleshy fruits fall on to the ground together with cleaned seeds previously ingested by primary dispersers, offering a wide range of fruits and seeds to the ground foragers. Although nutritional properties strongly differ between fruits and seeds, this different seed presentation (cleaned seeds versus seeds within the pulp) has not been addressed in seed removal studies. This study reports on the removal of fruits versus their seeds in five fleshy-fruited species in a temperate forest. We found that rodents removed most of the seeds and partially consumed most of the fruits, preferring seeds to fruits. Rodents bit the fruits to extract the seeds, leaving most of the pulp. We found a preference ranking for the seeds (Sorbus aucuparia>Ilex aquifolium>Sorbus aria>Rosa canina>Crataegus monogyna) but no preferences were found for the fruits, probably due to their similarities in pulp constituents. Seed and fruit choice were affected by chemical and physical properties and not by their size. The presence of alternative and preferred seeds (nuts) delayed the encounter of the fruits and seeds and diminished their removal rates. We found that higher rodent abundance is not necessarily associated with higher removal rates of fleshy fruits. Rodent abundance, fruit size and seed size are minor factors in the removal of fleshy fruits and their seeds. This study underlines that scatter-hoarding rodents are important removers of fleshy fruits and their seeds, producing a differential seed removal depending on the seed presentation (with or without pulp), the nutritional properties of the seeds (but not of the fruits) and the presence of alternative food

Growth dynamics and yield of melon as influenced by nitrogen fertilizer

Nitrogen (N) is an important nutrient for melon (Cucumis melo L.) production. However there is scanty information about the amount necessary to maintain an appropriate balance between growth and yield. Melon vegetative organs must develop sufficiently to intercept light and accumulate water and nutrients but it is also important to obtain a large reproductive-vegetative dry weight ratio to maximize the fruit yield. We evaluated the influence of different N amounts on the growth, production of dry matter and fruit yield of a melon ‘Piel de sapo’ type. A three-year field experiment was carried out from May to September. Melons were subjected to an irrigation depth of 100% crop evapotranspiration and to 11 N fertilization rates, ranging 11 to 393 kg ha –1 in the three years. The dry matter production of leaves and stems increased as the N amount increased. The dry matter of the whole plant was affected similarly, while the fruit dry matter decreased as the N amount was increased above 112, 93 and 95 kg ha –1 , in 2005, 2006 and 2007, respectively. The maximum Leaf Area Index (LAI), 3.1, was obtained at 393 kg ha –1 of N. The lowest N supply reduced the fruit yield by 21%, while the highest increased the vegetative growth, LAI and Leaf Area Duration (LAD), but reduced yield by 24% relative to the N93 treatment. Excessive applications of N increase vegetative growth at the expense of reproductive growth. For this melon type, rates about 90-100 kg ha –1 of N are sufficient for adequate plant growth, development and maximum production. To obtain fruit yield close to the maximum, the leaf N concentration at the end of the crop cycle should be higher than 19.5 g kg –1

Characterization of vineyard's canopy through fuzzy clustering and svm over color images

In this work we propose an image acquisition and processing methodology (framework) developed for performance in-field grapes and leaves detection and quantification, based on a six step methodology: 1) image segmentation through Fuzzy C-Means with Gustafson Kessel (FCM-GK) clustering; 2) obtaining of FCM-GK outputs (centroids) for acting as seeding for K-Means clustering; 3) Identification of the clusters generated by K-Means using a Support Vector Machine (SVM) classifier. 4) Performance of morphological operations over the grapes and leaves clusters in order to fill holes and to eliminate small pixels clusters; 5)Creation of a mosaic image by Scale-Invariant Feature Transform (SIFT) in order to avoid overlapping between images; 6) Calculation of the areas of leaves and grapes and finding of the centroids in the grape bunches. Image data are collected using a colour camera fixed to a mobile platform. This platform was developed to give a stabilized surface to guarantee that the images were acquired parallel to de vineyard rows. In this way, the platform avoids the distortion of the images that lead to poor estimation of the areas. Our preliminary results are promissory, although they still have shown that it is necessary to implement a camera stabilization system to avoid undesired camera movements, and also a parallel processing procedure in order to speed up the mosaicking process.

Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions

The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R2 values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management.

LiDAR simulation in modelled orchards to optimise the use of terretrial laser scanners and derived vegetative measures

Light detection and ranging (LiDAR) technology is beginning to have an impact on agriculture. Canopy volume and/or fruit tree leaf area can be estimated using terrestrial laser sensors based on this technology. However, the use of these devices may have different options depending on the resolution and scanning mode. As a consequence, data accuracy and LiDAR derived parameters are affected by sensor configuration, and may vary according to vegetative characteristics of tree crops. Given this scenario, users and suppliers of these devices need to know how to use the sensor in each case. This paper presents a computer program to determine the best configuration, allowing simulation and evaluation of different LiDAR configurations in various tree structures (or training systems). The ultimate goal is to optimise the use of laser scanners in field operations. The software presented generates a virtual orchard, and then allows the scanning simulation with a laser sensor. Trees are created using a hidden Markov tree (HMT) model. Varying the foliar structure of the orchard the LiDAR simulation was applied to twenty different artificially created orchards with or without leaves from two positions (lateral and zenith). To validate the laser sensor configuration, leaf surface of simulated trees was compared with the parameters obtained by LiDAR measurements: the impacted leaf area, the impacted total area (leaves and wood), and th impacted area in the three outer layers of leaves.

Distribution of indole-3-acetic acid in Petunia hybrida shoot tip cuttings and relationship between auxin transport, carbohydrate metabolism and adventitious root formation.

To determine the contribution of polar auxin transport (PAT) to auxin accumulation and to adventitious root (AR) formation in the stem base of Petunia hybrida shoot tip cuttings, the level of indole-3-acetic acid (IAA) was monitored in non-treated cuttings and cuttings treated with the auxin transport blocker naphthylphthalamic acid (NPA) and was complemented with precise anatomical studies. The temporal course of carbohydrates, amino acids and activities of controlling enzymes was also investigated. Analysis of initial spatial IAA distribution in the cuttings revealed that approximately 40 and 10% of the total IAA pool was present in the leaves and the stem base as rooting zone, respectively. A negative correlation existed between leaf size and IAA concentration. After excision of cuttings, IAA showed an early increase in the stem base with two peaks at 2 and 24h post excision and, thereafter, a decline to low levels. This was mirrored by the expression pattern of the auxin-responsive GH3 gene. NPA treatment completely suppressed the 24-h peak of IAA and severely inhibited root formation. It also reduced activities of cell wall and vacuolar invertases in the early phase of AR formation and inhibited the rise of activities of glucose-6-phosphate dehydrogenase and phosphofructokinase during later stages. We propose a model in which spontaneous AR formation in Petunia cuttings is dependent on PAT and on the resulting 24-h peak of IAA in the rooting zone, where it induces early cellular events and also stimulates sink establishment. Subsequent root development stimulates glycolysis and the pentosephosphate pathway

Transcriptional changes and oxidative stress associated with the synergistic interaction between potato virus X and potato virus Y and their relationship with symptom expression.

Many virus diseases of economic importance to agriculture result from mixtures of different pathogens invading the host at a given time. This contrasts with the relatively scarce studies available on the molecular events associated with virus---host interactions in mixed infections. Compared with single infections, co-infection of Nicotiana benthamiana with Potato virus X (PVX) and Potato virus Y (PVY) resulted in increased systemic symptoms (synergism) that led to necrosis of the newly emerging leaves and death of the plant. A comparative transcriptional analysis was undertaken to identify quantitative and qualitative differences in gene expression during this synergistic infection and correlate these changes with the severe symptoms it caused. Global transcription profiles of doubly infected leaves were compared with those from singly infected leaves using gene ontology enrichment analysis and metabolic pathway annotator software. Functional gene categories altered by the double infection comprise suites of genes regulated coordinately, which are associated with chloroplast functions (downregulated), protein synthesis and degradation (upregulated), carbohydrate metabolism (upregulated), and response to biotic stimulus and stress (upregulated). The expressions of reactive oxygen species?generating enzymes as well as several mitogen-activated protein kinases were also significantly induced. Accordingly, synergistic infection induced a severe oxidative stress in N. benthamiana leaves, as judged by increases in lipid peroxidation and by the generation of superoxide radicals in chloroplasts, which correlated with the misregulation of antioxidative genes in microarray data. Interestingly, expression of genes encoding oxylipin biosynthesis was uniquely upregulated by the synergistic infection. Virus-induced gene silencing of ?-dioxygenase1 delayed cell death during PVX?PVY infection.

Census, reproductive biology, and germination of Astragalus gines-lopezii (Fabaceae), a narrow and endangered endemic species of SW Spain

Astragalus gines-lopezii Talavera, Podlech, Devesa & F.M.Vázquez (Fabaceae) is a threatened endemic species with a distribution restricted to a very small area in Badajoz Province (Extremadura Region, SW Spain) and only 2 populations are known. This species was catalogued in the ?Endangered? category in the 2008 Red List and the 2010 Threatened Spanish Vascular Flora List. Despite its status as an endangered species, at present very little is known about the distribution, census, and reproductive biology of this species. In this study we have carried out an exhaustive census of A. gines-lopezii , and we have evaluated the production of flowers, fruits, and seeds and the existence or not of intra- and interpopulation variability in seed germination. Results have highlighted the high reproductive capacity of this species on the basis of a high production of flowers, fruits, and seeds. Mechanical scarification of seeds was effective for increasing germination. Thus, initial germination (22%?60%) was increased to 97%?99% when seeds were rubbed with sandpapers. A high intra- and interpopulation variability in seed germination was found in this species. A. gines-lopezii produces seeds with different degrees of physical dormancy, varying this grade among different individuals within a population.

Census, reproductive biology, and germination of Astragalus gines-lopezii (Fabaceae),a narrow and endangered endemic species of SW Spain.

Astragalus gines-lopezii Talavera, Podlech, Devesa & F.M.Vazquez (Fabaceae) is a threatened endemic species with a distribution restricted to a very small area in Badajoz Province (Extremadura Region, SW Spain) and only 2 populations are known.This species was catalogued in the "Endangered" category in the 2008 Red List and the 2010 Threatened Spanish Vascular Flora List. Despite its status as an endangered species, at present very little is known about the distribution, census, and reproductive biology of this species. In this study we have carried out anexhaustive census of A. gines-lopezii, and we have evaluated the production of flowers, fruits, and seeds and the existence or not of intra- and interpopulation variability in seed germination. Results have highlighted the high reproductive capacity of this species on the basis of a high production of flowers, fruits, and seeds. Mechanical scarification of seeds was effective for increasing germination. Thus, initial germination (22%-60%) was increased to 97%-99% when seeds were rubbed with sandpapers. A high intra- and interpopulation variability in seed germination was found in this species. A. gines-lopezii produces seeds with different degrees of physical dormancy, varying this grade among different individuals within a population.