The evolution of unicoloniality in ant societies : recognition, dispersal and population genetic structure

Introduction Societies of ants, bees, wasps and termites dominate many terrestrial ecosystems (Wilson 1971). Their evolutionary and ecological success is based upon the regulation of internal conflicts (e.g. Ratnieks et al. 2006), control of diseases (e.g. Schmid-Hempel 1998) and individual skills and collective intelligence in resource acquisition, nest building and defence (e.g. Camazine 2001). Individuals in social species can pass on their genes not only directly trough their own offspring, but also indirectly by favouring the reproduction of relatives. The inclusive fitness theory of Hamilton (1963; 1964) provides a powerful explanation for the evolution of reproductive altruism and cooperation in groups with related individuals. The same theory also led to the realization that insect societies are subject to internal conflicts over reproduction. Relatedness of less-than-one is not sufficient to eliminate all incentive for individual selfishness. This would indeed require a relatedness of one, as found among cells of an organism (Hardin 1968; Keller 1999). The challenge for evolutionary biology is to understand how groups can prevent or reduce the selfish exploitation of resources by group members, and how societies with low relatedness are maintained. In social insects the evolutionary shift from single- to multiple queens colonies modified the relatedness structure, the dispersal, and the mode of colony founding (e.g. (Crozier & Pamilo 1996). In ants, the most common, and presumably ancestral mode of reproduction is the emission of winged males and females, which found a new colony independently after mating and dispersal flights (Hölldobler & Wilson 1990). The alternative reproductive tactic for ant queens in multiple-queen colonies (polygyne) is to seek to be re-accepted in their natal colonies, where they may remain as additional reproductives or subsequently disperse on foot with part of the colony (budding) (Bourke & Franks 1995; Crozier & Pamilo 1996; Hölldobler & Wilson 1990). Such ant colonies can contain up to several hundred reproductive queens with an even more numerous workforce (Cherix 1980; Cherix 1983). As a consequence in polygynous ants the relatedness among nestmates is very low, and workers raise brood of queens to which they are only distantly related (Crozier & Pamilo 1996; Queller & Strassmann 1998). Therefore workers could increase their inclusive fitness by preferentially caring for their closest relatives and discriminate against less related or foreign individuals (Keller 1997; Queller & Strassmann 2002; Tarpy et al. 2004). However, the bulk of the evidence suggests that social insects do not behave nepotistically, probably because of the costs entailed by decreased colony efficiency or discrimination errors (Keller 1997). Recently, the consensus that nepotistic behaviour does not occur in insect colonies was challenged by a study in the ant Formica fusca (Hannonen & Sundström 2003b) showing that the reproductive share of queens more closely related to workers increases during brood development. However, this pattern can be explained either by nepotism with workers preferentially rearing the brood of more closely related queens or intrinsic differences in the viability of eggs laid by queens. In the first chapter, we designed an experiment to disentangle nepotism and differences in brood viability. We tested if workers prefer to rear their kin when given the choice between highly related and unrelated brood in the ant F. exsecta. We also looked for differences in egg viability among queens and simulated if such differences in egg viability may mistakenly lead to the conclusion that workers behave nepotistically. The acceptance of queens in polygnous ants raises the question whether the varying degree of relatedness affects their share in reproduction. In such colonies workers should favour nestmate queens over foreign queens. Numerous studies have investigated reproductive skew and partitioning of reproduction among queens (Bourke et al. 1997; Fournier et al. 2004; Fournier & Keller 2001; Hammond et al. 2006; Hannonen & Sundström 2003a; Heinze et al. 2001; Kümmerli & Keller 2007; Langer et al. 2004; Pamilo & Seppä 1994; Ross 1988; Ross 1993; Rüppell et al. 2002), yet almost no information is available on whether differences among queens in their relatedness to other colony members affects their share in reproduction. Such data are necessary to compare the relative reproductive success of dispersing and non-dispersing individuals. Moreover, information on whether there is a difference in reproductive success between resident and dispersing queens is also important for our understanding of the genetic structure of ant colonies and the dynamics of within group conflicts. In chapter two, we created single-queen colonies and then introduced a foreign queens originating from another colony kept under similar conditions in order to estimate the rate of queen acceptance into foreign established colonies, and to quantify the reproductive share of resident and introduced queens. An increasing number of studies have investigated the discrimination ability between ant workers (e.g. Holzer et al. 2006; Pedersen et al. 2006), but few have addressed the recognition and discrimination behaviour of workers towards reproductive individuals entering colonies (Bennett 1988; Brown et al. 2003; Evans 1996; Fortelius et al. 1993; Kikuchi et al. 2007; Rosengren & Pamilo 1986; Stuart et al. 1993; Sundström 1997; Vásquez & Silverman in press). These studies are important, because accepting new queens will generally have a large impact on colony kin structure and inclusive fitness of workers (Heinze & Keller 2000). In chapter three, we examined whether resident workers reject young foreign queens that enter into their nest. We introduced mated queens into their natal nest, a foreign-female producing nest, or a foreign male-producing nest and measured their survival. In addition, we also introduced young virgin and mated queens into their natal nest to examine whether the mating status of the queens influences their survival and acceptance by workers. On top of polgyny, some ant species have evolved an extraordinary social organization called 'unicoloniality' (Hölldobler & Wilson 1977; Pedersen et al. 2006). In unicolonial ants, intercolony borders are absent and workers and queens mix among the physically separated nests, such that nests form one large supercolony. Super-colonies can become very large, so that direct cooperative interactions are impossible between individuals of distant nests. Unicoloniality is an evolutionary paradox and a potential problem for kin selection theory because the mixing of queens and workers between nests leads to extremely low relatedness among nestmates (Bourke & Franks 1995; Crozier & Pamilo 1996; Keller 1995). A better understanding of the evolution and maintenance of unicoloniality requests detailed information on the discrimination behavior, dispersal, population structure, and the scale of competition. Cryptic genetic population structure may provide important information on the relevant scale to be considered when measuring relatedness and the role of kin selection. Theoretical studies have shown that relatedness should be measured at the level of the `economic neighborhood', which is the scale at which intraspecific competition generally takes place (Griffin & West 2002; Kelly 1994; Queller 1994; Taylor 1992). In chapter four, we conducted alarge-scale study to determine whether the unicolonial ant Formica paralugubris forms populations that are organised in discrete supercolonies or whether there is a continuous gradation in the level of aggression that may correlate with genetic isolation by distance and/or spatial distance between nests. In chapter five, we investigated the fine-scale population structure in three populations of F. paralugubris. We have developed mitochondria) markers, which together with the nuclear markers allowed us to detect cryptic genetic clusters of nests, to obtain more precise information on the genetic differentiation within populations, and to separate male and female gene flow. These new data provide important information on the scale to be considered when measuring relatedness in native unicolonial populations.

Automated identification of minimal myocardial motion for improved image quality on MR angiography at 3 T

OBJECTIVE: Imaging during a period of minimal myocardial motion is of paramount importance for coronary MR angiography (MRA). The objective of our study was to evaluate the utility of FREEZE, a custom-built automated tool for the identification of the period of minimal myocardial motion, in both a moving phantom at 1.5 T and 10 healthy adults (nine men, one woman; mean age, 24.9 years; age range, 21-32 years) at 3 T. CONCLUSION: Quantitative analysis of the moving phantom showed that dimension measurements approached those obtained in the static phantom when using FREEZE. In vitro, vessel sharpness, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were significantly improved when coronary MRA was performed during the software-prescribed period of minimal myocardial motion (p < 0.05). Consistent with these objective findings, image quality assessments by consensus review also improved significantly when using the automated prescription of the period of minimal myocardial motion. The use of FREEZE improves image quality of coronary MRA. Simultaneously, operator dependence can be minimized while the ease of use is improved.

Auditory localisation and recognition after left inferior collicular lesion: effects of work load on cortical activation patterns (fMRI study)

Evidence from neuropsychological and activation studies (Clarke et al., 2oo0, Maeder et al., 2000) suggests that sound recognitionand localisation are processed by two anatomically and functionally distinct cortical networks. We report here on a case of a patientthat had an interruption of auditory information and we show: i) the effects of this interruption on cortical auditory processing; ii)the effect of the workload on activation pattern.A 36 year old man suffered from a small left mesencephalic haemotrhage, due to cavernous angioma; the let% inferior colliculuswas resected in the surgical approach of the vascular malformation. In the acute stage, the patient complained of auditoryhallucinations and of auditory loss in right ear, while tonal audiometry was normal. At 12 months, auditory recognition, auditorylocalisation (assessed by lTD and IID cues) and auditory motion perception were normal (Clarke et al., 2000), while verbal dichoticlistening was deficient on the right side.Sound recognition and sound localisation activation patterns were investigated with fMRI, using a passive and an activeparadigm. In normal subjects, distinct cortical networks were involved in sound recognition and localisation, both in passive andactive paradigm (Maeder et al., 2OOOa, 2000b).Passive listening of environmental and spatial stimuli as compared to rest strongly activated right auditory cortex, but failed toactivate left primary auditory cortex. The specialised networks for sound recognition and localisation could not be visual&d onthe right and only minimally on the left convexity. A very different activation pattern was obtained in the active condition wherea motor response was required. Workload not only increased the activation of the right auditory cortex, but also allowed theactivation of the left primary auditory cortex. The specialised networks for sound recognition and localisation were almostcompletely present in both hemispheres.These results show that increasing the workload can i) help to recruit cortical region in the auditory deafferented hemisphere;and ii) lead to processing auditory information within specific cortical networks.References:Clarke et al. (2000). Neuropsychologia 38: 797-807.Mae.der et al. (2OOOa), Neuroimage 11: S52.Maeder et al. (2OOOb), Neuroimage 11: S33

Retinal Image Analysis Using Machine Vision

The topic of this thesis is studying how lesions in retina caused by diabetic retinopathy can be detected from color fundus images by using machine vision methods. Methods for equalizing uneven illumination in fundus images, detecting regions of poor image quality due toinadequate illumination, and recognizing abnormal lesions were developed duringthe work. The developed methods exploit mainly the color information and simpleshape features to detect lesions. In addition, a graphical tool for collecting lesion data was developed. The tool was used by an ophthalmologist who marked lesions in the images to help method development and evaluation. The tool is a general purpose one, and thus it is possible to reuse the tool in similar projects.The developed methods were tested with a separate test set of 128 color fundus images. From test results it was calculated how accurately methods classify abnormal funduses as abnormal (sensitivity) and healthy funduses as normal (specificity). The sensitivity values were 92% for hemorrhages, 73% for red small dots (microaneurysms and small hemorrhages), and 77% for exudates (hard and soft exudates). The specificity values were 75% for hemorrhages, 70% for red small dots, and 50% for exudates. Thus, the developed methods detected hemorrhages accurately and microaneurysms and exudates moderately.

Sidelobe elimination for generalized synthetic discriminant functions by a two-filter correlation and subsequent postprocessing of the intensity distributions

One of the most important problems in optical pattern recognition by correlation is the appearance of sidelobes in the correlation plane, which causes false alarms. We present a method that eliminate sidelobes of up to a given height if certain conditions are satisfied. The method can be applied to any generalized synthetic discriminant function filter and is capable of rejecting lateral peaks that are even higher than the central correlation. Satisfactory results were obtained in both computer simulations and optical implementation.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed

Security Authentication using Phase-Encoded Nanoparticle Structures and Polarized Light

Phase encoded nano structures such as Quick Response (QR) codes made of metallic nanoparticles are suggested to be used in security and authentication applications. We present a polarimetric optical method able to authenticate random phase encoded QR codes. The system is illuminated using polarized light and the QR code is encoded using a phase-only random mask. Using classification algorithms it is possible to validate the QR code from the examination of the polarimetric signature of the speckle pattern. We used Kolmogorov-Smirnov statistical test and Support Vector Machine algorithms to authenticate the phase encoded QR codes using polarimetric signatures.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

A concurrent region growing algorithm guided by circumscribed contours

Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the region's characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach

Performance Evaluation and Calibration of Machine Vision Measurement

This thesis presents the calibration and comparison of two systems, a machine vision system that uses 3 channel RGB images and a line scanning spectral system. Calibration. is the process of checking and adjusting the accuracy of a measuring instrument by comparing it with standards. For the RGB system self-calibrating methods for finding various parameters of the imaging device were developed. Color calibration was done and the colors produced by the system were compared to the known colors values of the target. Software drivers for the Sony Robot were also developed and a mechanical part to connect a camera to the robot was also designed. For the line scanning spectral system, methods for the calibrating the alignment of the system and the measurement of the dimensions of the line scanned by the system were developed. Color calibration of the spectral system is also presented.

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

Chemical pattern of brazilian apples: a chemometric approach based on the Fuji and Gala varieties

The chemical composition of apple juices may be used to discriminate between the varieties for consumption and those for raw material. Fuji and Gala have a chemical pattern that can be used for this classification. Multivariate methods correlate independent continuous chemical descriptors with the categorical apple variety. Three main descriptors of apple juice were selected: malic acid, total reducing sugar and total phenolic compounds. A chemometric approach, employing PCA and SIMCA, was used to classify apple juice samples. PCA was performed with 24 juices from Fuji and Gala, and SIMCA, with 15 juices. The exploratory and predictive models recognized 88% and 64%, respectively, as belonging to a mixed domain. The apple juice from commercial fruits shows a pattern related to cv. Fuji and Gala with boundaries from 0.18 to 0.389 g.100 mL-1 (malic acid), from 8.65 to 15.18 g.100 mL-1 (total reducing sugar) and from 100 to 400 mg.L-1 (total phenolic compounds), but such boundaries were slightly shorter in the remaining set of commercial apple juices, specifically from 0.16 to 0.36 g.100 mL-1, from 9.25 to 15.5 g.100 mL-1 and from 180 to 606 mg.L-1 for acidity, reducing sugar and phenolic compounds, respectively, representing the acid, sweet and bitter tastes.

A comparison of two computer-assisted remedial reading programs for adolescent unskilled readers : component reading skills and repeated reading

This study compared the relative effectiveness of two computerized remedial reading programs in improving the reading word recognition, rate, and comprehension of adolescent readers demonstrating significant and longstanding reading difficulties. One of the programs involved was Autoskill Component Reading Subskills Program, which provides instruction in isolated letters, syllables, and words, to a point of rapid automatic responding. This program also incorporates reading disability subtypes in its approach. The second program, Read It Again. Sam, delivers a repeated reading strategy. The study also examined the feasibility of using peer tutors in association with these two programs. Grade 9 students at a secondary vocational school who satisfied specific criteria with respect to cognitive and reading ability participated. Eighteen students were randomly assigned to three matched groups, based on prior screening on a battery of reading achievement tests. Two I I groups received training with one of the computer programs; the third group acted as a control and received the remedial reading program offered within the regular classroom. The groups met daily with a trained tutor for approximately 35 minutes, and were required to accumulate twenty hours of instruction. At the conclusion of the program, the pretest battery was repeated. No significant differences were found in the treatment effects of the two computer groups. Each of the two treatment groups was able to effect significantly improved reading word recognition and rate, relative to the control group. Comprehension gains were modest. The treatment groups demonstrated a significant gain, relative to the control group, on one of the three comprehension measures; only trends toward a gain were noted on the remaining two measures. The tutoring partnership appeared to be a viable alternative for the teacher seeking to provide individualized computerized remedial programs for adolescent unskilled readers. Both programs took advantage of computer technology in providing individualized drill and practice, instant feedback, and ongoing recordkeeping. With limited cautions, each of these programs was considered effective and practical for use with adolescent unskilled readers.