Nectar- and pollen-gathering Cephalotes ants provide no protection against herbivory: a new manipulative experiment to test ant protective capabilities

The question if ant behaviour and biological limitations should be considered before generalisations about the ant’s defensive capabilities in ant–plant relationships was explored through a new experimental manipulation. In the Brazilian tropical savanna, we tested the protective action of Cephalotes pusillus Klug on the extrafloral nectar-bearing plant Ouratea spectabilis Engl. (Ochnaceae). Three treatments were performed: control (free ant access), Cephalotes-treatment (access permitted only to C. pusillus), and ant free treatment (no ants). No difference was found in the levels of leaf herbivory among experimental stems. Visitation by different ant species to control stems translated into significantly greater fruit and seed production by this stem category than by ant-free and Cephalotes-treated stems. Thus, results showed that an investigation of system’s natural history, ant’s morphological traits, defensive capabilities and behaviour are needed before a protective role is inferred to each associated ant species.     

Weak hierarchies associated with similarity measures—An additive clustering technique

A new and apparently rather useful and natural concept in cluster analysis is studied: given a similarity measure on a set of objects, a sub-set is regarded as a cluster if any two objectsa, b inside this sub-set have greater similarity than any third object outside has to at least one ofa, b. These clusters then form a closure system which can be described as a hypergraph without triangles. Conversely, given such a system, one may attach some weight to each cluster and then compose a similarity measure additively, by letting the similarity of a pair be the sum of weights of the clusters containing that particular pair. The original clusters can be reconstructed from the obtained similarity measure. This clustering model is thus located between the general additive clustering model of Shepard and Arabie (1979) and the standard hierarchical model. Potential applications include fitting dendrograms with few additional nonnested clusters and simultaneous representation of some families of multiple dendrograms (in particular, two-dendrogram solutions), as well as assisting the search for phylogenetic relationships by proposing a somewhat larger system of possibly relevant “family groups”, from which an appropriate choice (based on additional insight or individual preferences) remains to be made.     

Computational model for perception of objects and motions

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist ‘What’ and ‘Where’ pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives ‘where’, for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The computational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.     

Selective integration of multiple biological data for supervised network inference

MOTIVATION: Inferring networks of proteins from biological data is a central issue of computational biology. Most network inference methods, including Bayesian networks, take unsupervised approaches in which the network is totally unknown in the beginning, and all the edges have to be predicted. A more realistic supervised framework, proposed recently, assumes that a substantial part of the network is known. We propose a new kernel-based method for supervised graph inference based on multiple types of biological datasets such as gene expression, phylogenetic profiles and amino acid sequences. Notably, our method assigns a weight to each type of dataset and thereby selects informative ones. Data selection is useful for reducing data collection costs. For example, when a similar network inference problem must be solved for other organisms, the dataset excluded by our algorithm need not be collected. RESULTS: First, we formulate supervised network inference as a kernel matrix completion problem, where the inference of edges boils down to estimation of missing entries of a kernel matrix. Then, an expectation-maximization algorithm is proposed to simultaneously infer the missing entries of the kernel matrix and the weights of multiple datasets. By introducing the weights, we can integrate multiple datasets selectively and thereby exclude irrelevant and noisy datasets. Our approach is favorably tested in two biological networks: a metabolic network and a protein interaction network. AVAILABILITY: Software is available on request.     

Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules

Object recognition requires the solution of the binding and segmentation problems, i.e., grouping different features to achieve a coherent representation. Synchronization of neural activity in the gamma-band, associated with gestalt perception, has often been proposed as a putative mechanism to solve these problems, not only as to low-level processing, but also in higher cortical functions. In the present work, a network of Wilson-Cowan oscillators is used to segment simultaneous objects, and recover an object from partial or corrupted information, by implementing two gestalt rules: similarity and prior knowledge. The network consists of H different areas, each devoted to representation of a particular feature of the object, according to a topological organization. The similarity law is realized via lateral intra-area connections, arranged as a "Mexican-hat". Prior knowledge is realized via inter-area connections, which link properties belonging to a previously memorized object. A global inhibitor allows segmentation of several objects avoiding interference. Simulation results, performed using three simultaneous input objects, show that the network is able to detect an object even in difficult conditions (i.e., when some features are absent or shifted with respect to the original one). Moreover, the trade-off between sensitivity (capacity to detect true positives) and specificity (capacity to reject false positives) can be controlled acting on the extension of lateral synapses (i.e., on the level of accepted similarity). Finally, the network can also deal with correlated objects, i.e., objects which have some common features. Simulations performed using a different number of objects (2, 3, 4 or 5) suggest that the network is able to segment and recall up to four objects, but the oscillation frequency must increase, the lower the number of objects simultaneously present. The model, although quite simpler compared with neurophysiology, may represent a theoretical framework for the analysis of the relationships between object representation, memory, learning, and gamma-band activity. In particular, it extends previous studies on autoassociative memory since it exploits not only oscillatory dynamics, but also a topological organization of features.     

Nonparametric density estimation and regression achieved with topographic maps maximizing the information-theoretic entropy of their outputs

We introduce an unsupervised competitive learning rule, called the extended Maximum Entropy learning Rule (eMER), for topographic map formation. Unlike Kohonen's Self-Organizing Map (SOM) algorithm, the presence of a neighborhood function is not a prerequisite for achieving topology-preserving mappings, but instead it is intended: (1) to speed up the learning process and (2) to perform nonparametric regression. We show that, when the neighborhood function vanishes, the neural weigh t density at convergence approaches a linear function of the input density so that the map can be regarded as a nonparametric model of the input density. We apply eMER to density estimation and compare its performance with that of the SOM algorithm and the variable kernel method. Finally, we apply the ‘batch’ version of eMER to nonparametric projection pursuit regression and compare its performance with that of back-propagation learning, projection pursuit learning, constrained topolog ical mapping, and the Heskes and Kappen approach. Received: 12 August 1996 / Accepted in revised form: 9 April 1997     

Do extrafloral nectar resources, species abundances, and body sizes contribute to the structure of ant–plant mutualistic networks?

Recent research has shown that many mutualistic communities display non-random structures. While our understanding of the structural properties of mutualistic communities continues to improve, we know little of the biological variables resulting in them. Mutualistic communities include those formed between ants and extrafloral (EF) nectar-bearing plants. In this study, we examined the contributions of plant and ant abundance, plant and ant size, and plant EF nectar resources to the network structures of nestedness and interaction frequency of ant–plant networks across five sites within one geographic locality in the Sonoran Desert. Interactions between ant and plant species were largely symmetric. That is, ant and plant species exerted nearly equivalent quantitative interaction effects on one another, as measured by their frequency of interaction. The mutualistic ant–plant networks also showed nested patterns of structure, in which there was a central core of generalist ant and plant species interacting with one another and few specialist–specialist interactions. Abundance and plant size and ant body size were the best predictors of symmetric interactions between plants and ants, as well as nestedness. Despite interactions in these communities being ultimately mediated by EF nectar resources, the number of EF nectaries had a relatively weak ability to explain variation in symmetric interactions and nestedness. These results suggest that different mechanisms may contribute to structure of bipartite networks. Moreover, our results for ant–plant mutualistic networks support the general importance of species abundances for the structure of species interactions within biological communities.     

A multiple kernel density clustering algorithm for incomplete datasets in bioinformatics

Background

While there are a large number of bioinformatics datasets for clustering, many of them are incomplete, i.e., missing attribute values in some data samples needed by clustering algorithms. A variety of clustering algorithms have been proposed in the past years, but they usually are limited to cluster on the complete dataset. Besides, conventional clustering algorithms cannot obtain a trade-off between accuracy and efficiency of the clustering process since many essential parameters are determined by the human user’s experience.

Results

The paper proposes a Multiple Kernel Density Clustering algorithm for Incomplete datasets called MKDCI. The MKDCI algorithm consists of recovering missing attribute values of input data samples, learning an optimally combined kernel for clustering the input dataset, reducing dimensionality with the optimal kernel based on multiple basis kernels, detecting cluster centroids with the Isolation Forests method, assigning clusters with arbitrary shape and visualizing the results.

Conclusions

Extensive experiments on several well-known clustering datasets in bioinformatics field demonstrate the effectiveness of the proposed MKDCI algorithm. Compared with existing density clustering algorithms and parameter-free clustering algorithms, the proposed MKDCI algorithm tends to automatically produce clusters of better quality on the incomplete dataset in bioinformatics.

     

Simulated visual homing in desert ant natural environments: efficiency of skyline cues

Desert ants, foraging in cluttered semiarid environments, are thought to be visually guided along individual, habitual routes. While other navigational mechanisms (e.g. path integration) are well studied, the question of how ants extract reliable visual features from a complex visual scene is still largely open. This paper explores the assumption that the upper outline of ground objects formed against the sky, i.e. the skyline, provides sufficient information for visual navigation. We constructed a virtual model of the ant’s environment. In the virtual environment, panoramic images were recorded and adapted to the resolution of the desert ant’s complex eye. From these images either a skyline code or a pixel-based intensity code were extracted. Further, two homing algorithms were implemented, a modified version of the average landmark vector (ALV) model (Lambrinos et al. Robot Auton Syst 30:39–64, 2000) and a gradient ascent method. Results show less spatial aliasing for skyline coding and best homing performance for ALV homing based on skyline codes. This supports the assumption of skyline coding in visual homing of desert ants and allows novel approaches to technical outdoor navigation.     

Data reduction for spectral clustering to analyze high throughput flow cytometry data

Background  

Recent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address this issue, we have modified spectral clustering by adding an information preserving sampling procedure and applying a post-processing stage. We call this entire algorithm SamSPECTRAL.     

Order Parameter Dynamics of Body-scaled Hysteresis and Mode Transitions in Grasping Behavior

Several experimental studies have shown that human grasping behavior exhibits a transition from one-handed to two-handed grasping when to-be-grasped objects become larger and larger. The transition point depends on the relative size of objects measured in terms of human body-scales. Most strikingly, the transitions between the two different behavioral ‘modes’ of grasping exhibit hysteresis. That is, one-to-two hand transitions and two-to-one hand transitions occur at different relative object sizes when objects are scaled up or down in size. In our study we approach body-scaled hysteresis and mode transitions in grasping by exploiting the notion that human behavior in general results from self-organization and satisfies appropriately-defined order parameter equations. To this end, grasping transitions and grasping hysteresis are discussed from a theoretical perspective in analogy to cognitive processes defined by Haken’s neural network model for pattern recognition. In doing so, issues such as the exclusivity of grasping modes, biomechanical constraints, mode-mode interactions, single subject behavior and population behavior are explored.     

Long-term temporal variation in the organization of an ant–plant network

Background and Aims

Functional groups of species interact and coevolve in space and time, forming complex networks of interacting species. A long-term study of temporal variation of an ant–plant network is presented with the aims of: (1) depicting its structural changes over a 20-year period; (2) detailing temporal variation in network topology, as revealed by nestedness and modularity analysis and other parameters (i.e. connectance, niche overlap); and (3) identifying long-term turnover in taxonomic structure (i.e. switches in ant resource use or plant visitor assemblages according to taxa).

Methods

Fieldwork was carried out at La Mancha, Mexico, and ant–plant interactions were observed between 1989 and 1991, between 1998 and 2000, and between May 2010 and 2011. Occurrences of ants on extrafloral nectaries (EFNs) were recorded. The resulting ant–plant networks were constructed from qualitative presence–absence data determined by a species–species matrix defined by the frequency of occurrence of each pairwise ant–plant interaction.

Key Results

Network variation across time was stable and a persistent nested structure may have contributed to the maintenance of resilient and species-rich communities. Modularity was lower than expected, especially in the most recent networks, indicating that the community exhibited high overlap among interacting species (e.g. few species were hubs in the more recent network, being partly responsible for the nested pattern). Structurally, the connections created among modules by super-generalists gave cohesion to subsets of species that otherwise would remain unconnected. This may have allowed an increasing cascade-effect of evolutionary events among modules. Mutualistic ant–plant interactions were structured 20 years ago mainly by the subdominant nectarivorous ant species Camponotus planatus and Crematogaster brevispinosa, which monopolized the best extrafloral nectar resources and out-competed other species with broader feeding habits. Through time, these ants, which are still present, lost their position as network hubs and diminished in their importance in structuring the network; simultaneously, plants gained in importance.

Conclusions

The long-term network analysis reveals a decrease in attended plant species richness, a notable increase in plant species participation from 1990 to 2010 (sustained by less plant taxonomic similarity in the older 1990 network), an increase in the number of ant species and a diminishing dominance of super-generalist ants. The structure of the community has remained highly nested and connected with low modularity, suggesting overall a more participative, homogeneous, cohesive interaction network. Although previous studies have suggested that interactions between ants and EFN-bearing plants are susceptible to seasonality, abiotic factors and perturbation, this cohesive structure appears to be the key for biodiversity and community maintenance.     

Support vector machine regression for the prediction of maize hybrid performance

Accurate prediction of the phenotypical performance of untested single-cross hybrids allows for a faster genetic progress of the breeding pool at a reduced cost. We propose a prediction method based on ɛ-insensitive support vector machine regression (ɛ-SVR). A brief overview of the theoretical background of this fairly new technique and the use of specific kernel functions based on commonly applied genetic similarity measures for dominant and co-dominant markers are presented. These different marker types can be integrated into a single regression model by means of simple kernel operations. Field trial data from the grain maize breeding programme of the private company RAGT R2n are used to assess the predictive capabilities of the proposed methodology. Prediction accuracies are compared to those of one of today’s best performing prediction methods based on best linear unbiased prediction. Results on our data indicate that both methods match each other’s prediction accuracies for several combinations of marker types and traits. The ɛ-SVR framework, however, allows for a greater flexibility in combining different kinds of predictor variables.     

Developing classification in action: II. Young chimpanzees (Pan troglodytes)

The development of spontaneous object manipulation in 5 chimpanzees (Pan troglodytes) from ages 15 to 54 months was investigated, focusing on formal properties of subjects’ acts and the objects they manipulated. Young chimpanzees’ manipulation progress from serial one-at-a-time acts on one object to parallel two-at-a-time acts on two or more objects. With age, simultaneous acts become increasingly transformational and identical or reciprocal to each other. Moreover, the class properties of objects manipulated simultaneously change. When presented with objects belonging to two different classes, subjects shift, with age, from manipulating different objects to manipulating identical or similar objects. In all these respects young chimpanzee’ development is similar to human infants’. In others it differs. Most especially, the onset age is later and the development is slower as well as less structurally complex.     

Opening myrmecochory’s black box: what happens inside the ant nest?

In the process of seed dispersal by ants (myrmecochory), foragers bring diaspores back to their nest, then eat the elaiosome and usually reject viable seeds outside the nest. Here, we investigate what happens inside the nest, a barely known stage of the myrmecochory process, for two seed species (Viola odorata, Chelidonium majus) dispersed either by the insectivorous ant Myrmica rubra or by the aphid-tending ant Lasius niger. Globally, elaiosome detachment decreased ants’ interest towards seeds and increased their probability of rejecting them. However, we found marked differences in seed management by ants inside the nest. The dynamics of elaiosome detachment were ant- and plant-specific whereas the dynamic of seed rejection were mainly ant-specific. Seeds remained for a shorter period of time inside the nest of the carnivorous ant Myrmica rubra than in Lasius niger nest. Thus, elaiosome detachment and seed rejection were two competing dynamics whose relative efficiency leads to variable outcomes in terms of types of dispersed items and of nutrient benefit to the ants. This is why some seeds remained inside the nest even without an elaiosome, and conversely, some seeds were rejected with an elaiosome still attached. Fresh seeds may be deposited directly in contact with the larvae. However, the dynamics of larvae-seeds contacts were also highly variable among species. This study illustrates the complexity and variability of the ecological network of ant–seed interactions.     

A novel method of spectral clustering in attributed networks by constructing parameter-free affinity matrix

The most basic and significant issue in complex network analysis is community detection, which is a branch of machine learning. Most current community detection approaches, only consider a network's topology structures, which lose the potential to use node attribute information. In attributed networks, both topological structure and node attributed are important features for community detection. In recent years, the spectral clustering algorithm has received much interest as one of the best performing algorithms in the subcategory of dimensionality reduction. This algorithm applies the eigenvalues of the affinity matrix to map data to low-dimensional space. In the present paper, a new version of the spectral cluster, named Attributed Spectral Clustering (ASC), is applied for attributed graphs that the identified communities have structural cohesiveness and attribute homogeneity. Since the performance of spectral clustering heavily depends on the goodness of the affinity matrix, the ASC algorithm will use the Topological and Attribute Random Walk Affinity Matrix (TARWAM) as a new affinity matrix to calculate the similarity between nodes. TARWAM utilizes the biased random walk to integrate network topology and attribute information. It can improve the similarity degree among the pairs of nodes in the same density region of the attributed network, without the need for parameter tuning. The proposed approach has been compared to other primary and new attributed graph clustering algorithms based on synthetic and real datasets. The experimental results show that the proposed approach is more effective and accurate compared to other state-of-the-art attributed graph clustering techniques.

     

A neural network model of semantic memory linking feature-based object representation and words

Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via γ-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).     

Fine-Scale Dissection of Functional Protein Network Organization by Statistical Network Analysis

Revealing organizational principles of biological networks is an important goal of systems biology. In this study, we sought to analyze the dynamic organizational principles within the protein interaction network by studying the characteristics of individual neighborhoods of proteins within the network based on their gene expression as well as protein-protein interaction patterns. By clustering proteins into distinct groups based on their neighborhood gene expression characteristics, we identify several significant trends in the dynamic organization of the protein interaction network. We show that proteins with distinct neighborhood gene expression characteristics are positioned in specific localities in the protein interaction network thereby playing specific roles in the dynamic network connectivity. Remarkably, our analysis reveals a neighborhood characteristic that corresponds to the most centrally located group of proteins within the network. Further, we show that the connectivity pattern displayed by this group is consistent with the notion of “rich club connectivity” in complex networks. Importantly, our findings are largely reproducible in networks constructed using independent and different datasets.     

Size matters: mole (Talpa europaea) hills and nest-site selection of the ant Formica exsecta

Moles are fossorial mammals that can act both as zoogeomorphic agents and species diversity drivers. These popular animals regularly push heaps of earth from their subterranean tunnel systems to the surface. Thereby they rearrange and improve the local microtopography for ant nesting. Here we use a strongly molehill (Talpa europaea) mediated nest system of the unicolonial wood ant Formica (Coptoformica) exsecta to test for ecological factors influencing nest-site selection at the microhabitat scale. Our results show that the size of molehills plays an important role in the multifactorial process of the ant’s nest-site choice with solar insolation as a paramount factor. The ants clearly favored larger and better sun-exposed molehills, suggesting that the coaction of a zoogeomorphic modified microrelief and solar insolation can drive the spatial colonization of F. exsecta.