Indices of competition and bio-agroeconomic efficiency of lettuce and tomato intercrops in greenhouses

Intercropping is a cropping system for the production of greenhouse vegetables. It uses space more efficiently, thus reducing the cost of production. Intercropping tomato and lettuce has not been studied, but knowledge of the competitive and agroeconomic indices of these vegetables can help in the management of the intercropping system. The objectives of this study were to assess, through biological and agroeconomic indices, the competition between species and the profitability of intercropping tomato and lettuce at different times of transplantation over two growing seasons (autumn-winter and summer-winter) in greenhouse conditions. In autumn-winter, two experiments were conducted with a randomised complete-block design and five replicates. Tomato and lettuce were the main crops in the individual experiments. Treatments were arranged in a factorial of two cropping systems (intercropping and individual crops) with four transplants of the secondary crop (0, 10, 20 and 30 days after) plus an additional treatment (individual main crop). These two experiments were repeated in summer-winter. Tomato was the dominant crop regardless of transplant order. Intercropping systems established with transplants of both species on the same day had higher values of indices of competition and bio-agroeconomic efficiency than systems with longer periods of transplants between main and secondary crops. The intercropping of lettuce and tomato in greenhouses, regardless of transplant time or order, had bio-agroeconomic advantages over individual crops. The transplantation of tomato after lettuce is recommended for greater profitability.

Competition and phylogeny determine community structure in Mullerian co-mimics

Until recently, the study of negative and antagonistic interactions (for example, competition and predation) has dominated our understanding of community structure, maintenance and assembly(1). Nevertheless, a recent theoretical model suggests that positive interactions (for example, mutualisms) may counterbalance competition, facilitating long-term coexistence even among ecologically undifferentiated species(2). Mullerian mimics are mutualists that share the costs of predator education(3) and are therefore ideally suited for the investigation of positive and negative interactions in community dynamics. The sole empirical test of this model in a Mullerian mimetic community supports the prediction that positive interactions outweigh the negative effects of spatial overlap(4) (without quantifying resource acquisition). Understanding the role of trophic niche partitioning in facilitating the evolution and stability of Mullerian mimetic communities is now of critical importance, but has yet to be formally investigated. Here we show that resource partitioning and phylogeny determine community structure and outweigh the positive effects of Mullerian mimicry in a species-rich group of neotropical catfishes. From multiple, independent reproductively isolated allopatric communities displaying convergently evolved colour patterns, 92% consist of species that do not compete for resources. Significant differences in phylogenetically conserved traits (snout morphology and body size) were consistently linked to trait-specific resource acquisition. Thus, we report the first evidence, to our knowledge, that competition for trophic resources and phylogeny are pivotal factors in the stable evolution of Mullerian mimicry rings. More generally, our work demonstrates that competition for resources is likely to have a dominant role in the structuring of communities that are simultaneously subject to the effects of both positive and negative interactions.

Relationship between global structural parameters and Enzyme Commission hierarchy: Implications for function prediction

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Finns growth dynamics, competition and power-law scaling

We study the growth dynamics of the size of manufacturing firms considering competition and normal distribution of competency. We start with the fact that all components of the system struggle with each other for growth as happened in real competitive business world. The detailed quantitative agreement of the theory with empirical results of firms growth based on a large economic database spanning over 20 years is good with a single set of the parameters for all the curves. Further, the empirical data of the variation of the standard deviation of the growth rate with the size of the firm are in accordance with the present theory rather than a simple power law. (C) 2003 Elsevier B.V. B.V. All rights reserved.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Interference competition and scavenging by Crematogaster ants (Hymenoptera: Formicidae) associated with the webs of the social spider Anelosimus eximius (Araneae: Theridiidae) in the central Amazon

Crematogaster cf. victima is a common inhabitant in the sheet web nests of the social spider Anelosimus eximius in the central Amazon basin near Manaus. A number of other ant species were found foraging on the non-sticky webs of A. eximius, but none of these reached the web occupation frequency found in C. cf. victima, nor, with the exception of an unidentified species of Pheidole, did they form satellite nests in the web, as did this species. Many prey which escaped the knock-down threads of the sheet web of A. eximius colonies were captured by ants in the lower web portions which they dominated. Furthermore, prey which were rejected by A. eximius, especially large, heavily sclerotized beetles, were also consumed by this ant. Repeated observations and experiments suggest that C. cf. victima is able to deter A. eximius activity through aerial venom release. Resources lost by A. eximius colonies to ants, especially C. cf. victima, in colonial web area and prey, may pose significant costs and may reduce colony growth.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

Ecological competition and the incidence of Acinetobacter baumannii bloodstream infections in a teaching hospital in Southeastern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Particle competition and cooperation for semi-supervised learning with label noise

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)