Covariation between colony social structure and immune defences of workers in the ant Formica selysi

Several ant species vary in the number of queens per colony, yet the causes and consequences of this variation remain poorly understood. In previous experiments, we found that Formica selysi workers originating from multiple-queen (=polygyne) colonies had a lower resistance to a fungal pathogen than workers originating from single-queen (=monogyne) colonies. In contrast, group diversity improved disease resistance in experimental colonies. This discrepancy between field and experimental colonies suggested that variation in social structure in the field had antagonistic effects on worker resistance, possibly through a down-regulation of the immune system balancing the positive effect of genetic diversity. Here, we examined if workers originating from field colonies with alternative social structure differed in three major components of their immune system. We found that workers from polygyne colonies had a lower bacterial growth inhibitory activity than workers from monogyne colonies. In contrast, workers from the two types of colonies did not differ significantly in bacterial cell wall lytic activity and prophenoloxidase activity. Overall, the presence of multiple queens in a colony correlated with a slight reduction in one inducible component of the immune system of individual workers. This reduced level of immune defence might explain the lower resistance of workers originating from polygyne colonies despite the positive effect of genetic diversity. More generally, these results indicate that social changes at the group level can modulate individual immune defences.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Mutations in the colony stimulating factor 1 receptor (CSF1R) gene cause hereditary diffuse leukoencephalopathy with spheroids.

Hereditary diffuse leukoencephalopathy with spheroids (HDLS) is an autosomal-dominant central nervous system white-matter disease with variable clinical presentations, including personality and behavioral changes, dementia, depression, parkinsonism, seizures and other phenotypes. We combined genome-wide linkage analysis with exome sequencing and identified 14 different mutations affecting the tyrosine kinase domain of the colony stimulating factor 1 receptor (encoded by CSF1R) in 14 families with HDLS. In one kindred, we confirmed the de novo occurrence of the mutation. Follow-up sequencing identified an additional CSF1R mutation in an individual diagnosed with corticobasal syndrome. In vitro, CSF-1 stimulation resulted in rapid autophosphorylation of selected tyrosine residues in the kinase domain of wild-type but not mutant CSF1R, suggesting that HDLS may result from partial loss of CSF1R function. As CSF1R is a crucial mediator of microglial proliferation and differentiation in the brain, our findings suggest an important role for microglial dysfunction in HDLS pathogenesis.

Sociogenomics of Cooperation and Conflict during Colony Founding in the Fire Ant Solenopsis invicta.

One of the fundamental questions in biology is how cooperative and altruistic behaviors evolved. The majority of studies seeking to identify the genes regulating these behaviors have been performed in systems where behavioral and physiological differences are relatively fixed, such as in the honey bee. During colony founding in the monogyne (one queen per colony) social form of the fire ant Solenopsis invicta, newly-mated queens may start new colonies either individually (haplometrosis) or in groups (pleometrosis). However, only one queen (the "winner") in pleometrotic associations survives and takes the lead of the young colony while the others (the "losers") are executed. Thus, colony founding in fire ants provides an excellent system in which to examine the genes underpinning cooperative behavior and how the social environment shapes the expression of these genes. We developed a new whole genome microarray platform for S. invicta to characterize the gene expression patterns associated with colony founding behavior. First, we compared haplometrotic queens, pleometrotic winners and pleometrotic losers. Second, we manipulated pleometrotic couples in order to switch or maintain the social ranks of the two cofoundresses. Haplometrotic and pleometrotic queens differed in the expression of genes involved in stress response, aging, immunity, reproduction and lipid biosynthesis. Smaller sets of genes were differentially expressed between winners and losers. In the second experiment, switching social rank had a much greater impact on gene expression patterns than the initial/final rank. Expression differences for several candidate genes involved in key biological processes were confirmed using qRT-PCR. Our findings indicate that, in S. invicta, social environment plays a major role in the determination of the patterns of gene expression, while the queen's physiological state is secondary. These results highlight the powerful influence of social environment on regulation of the genomic state, physiology and ultimately, social behavior of animals.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Impact of helpers on colony productivity in a primitively eusocial bee

Small societies of totipotent individuals are good systems in which to study the costs and benefits of group living that are central to the origin and maintenance of eusociality. For instance, in eusocial halictid bees, some females remain in their natal nest to help rear the next brood. Why do helpers stay in the nest? Do they really help, and if yes, is their contribution large enough to voluntarily forfeit direct reproduction? Here, we estimate the impact of helpers on colony survival and productivity in the sweat bee Halictus scabiosae. The number of helpers was positively associated with colony survival and productivity. Colonies from which we experimentally removed one helper produced significantly fewer offspring. However, the effect of helper removal was very small, on average. From the removal experiment, we estimated that one helper increased colony productivity by 0.72 additional offspring in colonies with one to three helpers, while the increase was smaller and not statistically significant in larger colonies. We conclude that helpers do actually help in this primitively eusocial bee, particularly in small colonies. However, the resulting increase in colony productivity is low, which suggests that helpers may be constrained in their role or may attempt to reproduce.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Non-nest mate discrimination and clonal colony structure in the parthenogenetic ant Cerapachys biroi

Understanding the interplay between cooperation and conflict in social groups is a major goal of biology. One important factor is genetic relatedness, and animal societies are usually composed of related but genetically different individuals, setting the stage for conflicts over reproductive allocation. Recently, however, it has been found that several ant species reproduce predominantly asexually. Although this can potentially give rise to clonal societies, in the few well-studied cases, colonies are often chimeric assemblies of different genotypes, due to worker drifting or colony fusion. In the ant Cerapachys biroi, queens are absent and all individuals reproduce via thelytokous parthenogenesis, making this species an ideal study system of asexual reproduction and its consequences for social dynamics. Here, we show that colonies in our study population on Okinawa, Japan, recognize and effectively discriminate against foreign workers, especially those from unrelated asexual lineages. In accord with this finding, colonies never contained more than a single asexual lineage and average pairwise genetic relatedness within colonies was extremely high (r = 0.99). This implies that the scope for social conflict in C. biroi is limited, with unusually high potential for cooperation and altruism.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.