No abstract

Electrical Conductivity (EC) is a promising vigor test since it produces fast laboratory results. In sunflower, the leakage of electrolytes from the pericarp may interfere with exudates from embryo tissues. The aims of this work were (1) To determine the utility of the EC test using dehulled (without pericap) sunflower seeds to evaluate the vigor in different genotypes, exposed to contrasting seed filling period and storage conditions, (2) To explore the relationship between EC and germination values near post harvest and across storage period and (3) To propose ranges of vigor through EC, so as to categorize sunflower seeds lots. Seeds of commercial hybrids differentiated by acid composition (high and standard oleic acid), exploring contrasting seed filling period and storage conditions, were evaluated by EC, Tetrazolium (TZ-V) and Germination (G), near post harvest time and during storage period (1, 5, 9, 13 and 19 months). An independent set of 18 genotypes, stored during 1-108 months, were also analyzed for EC and G. Electric conductivity in dehulled seeds was effective to identify vigor differences of genotypes in different seed filling period and storage conditions. A general relationship between the loss of germination and vigor was established for sunflower. The ranges of vigor based on EC proposed for sunflower seeds classification were <70 µs cmG 1 gG 1 for high, 70-110 µs cmG 1 gG 1 for intermediate and >110 µs cmG 1 gG 1 for low vigor levels. It is the first report based on EC ranges to categorize the seed vigor of sunflower seeds lots.

Bird vocalizations have been the focus of a wide variety of interdisciplinary studies in bioacoustics and neuroethology since they serve as models of motor control, learning and auditory perception. Yet, researchers have only begun to shed light on the structure and function of birdsong. Hypotheses abound, but still there is little agreement as how songs should be analyzed. One of the main challenges has been to classify acoustic units (syllables) from birdsong recordings, a task requiring robust classification algorithms capable of generalizing to unseen instances and dealing with data scarcity. Systematically detecting changes in syllable repertoires can help biologists to understand the origin and evolution of birdsong.  The process of learning good features to discriminate among numerous and different sound classes is computationally expensive. Moreover, it might be impossible to achieve acceptable performance in cases where training data is scarce and classes are unbalanced. To address this issue, we propose a few-shot learning task in which an algorithm must make predictions given only a few instances of each class. We compared the performance of different Siamese Neural Networks at metric learning over the set of Cassini's Vireo syllables. Then, the network features were reused for the few-shot classification task. With this approach we overcame the limitations of data scarcity and class imbalance while achieving state-of-the-art performance.

International audienceA discriminant analysis was performed on a set of morphological characteristics for a group of Argentine commercial maize (Zea mays L.) hybrids. In the discriminant analysis a set of canonical varieties was constructed to assess the discriminatory incidence of those characteristics among commercial hybrids. Discriminant analyses were performed on plant, ear and kernel characters. With the exception of whole plant traits, significant discriminations were found and the composition of the canonical variables explaining them was determined. For traits measured on the whole plant, ear insertion height was primarily responsible for the variation, and a noticeable level of uniformity was observed with respect to stalk diameters. Ear traits (ear medium diameter and ear length), kernel weight and number of kernels, exhibited the most significant discrimination among hybrids. Finally, with respect to kernel traits, significant distances among hybrids were detected, and kernel length and kernel breadth were the most important traits.Détermination de caractères discriminants dans des hybrides de maïs. Une analyse discriminante a été faite pour un ensemble de caractères morphologiques chez un groupe d'hybrides commerciaux de maïs argentins (Zea mays L.). Avec cette analyse, une série de variables canoniques a été définie pour évaluer la capacité discriminante de ces caractères entre les hybrides. Des analyses discriminantes ont été réalisées pour des caractères de plante entière, de l'épi et du grain. A l'exception des caractères correspondants à la plante entière, des discriminations significatives ont été trouvées pour tous les caractères étudiés et la composition des variables canoniques pour les expliquer a été définie. Entre les caractères mesurés pour la plante entière, la hauteur d'insertion de l'épi a été le principal responsable de la variation, tandis que le diamètre de la tige a été le plus stable. Les caractères mesurés pour l'épi (diamètre moyen et longueur), le poids du grain et le nombre de grains ont été les plus significatives entre les hybrides. Finalement, des distances significatives entre hybrides ont été détectées avec la longueur et la largeur du grain