Woodland Cottage, or, Henry and Emma Acton's conversations to their children, on the animal and vegetable world. /

Publisher's advetisements on back cover.

Scientific lectures,

On flowers and insects.--On plants and insects.--On the habits of ants.--Attributes of ants.--Introduction of the study of prehistoric archæology.--Address to the Wiltshire archæological and natural history society.--Inaugural address to the Institute of bankers.

Imported fire ant : quarantine treatments for nursery stock and other regulated articles.

"Issued 1999"--P. [2] of cover.

Die psychischen Fähigkeiten der Ameisen und einiger anderer Insekten; mit einem Anhang über die Eigentümlichkeiten des Geruchsinnes bei jenen Tieren. Vorträge gehalten den 13. August 1901 am V. Internationalen Zoologen-Kongress zu Berlin.

Mode of access: Internet.

Le Brésil et ses ports; conférence de M. Édouard Quellennec ...

At head of title: Fédération des industriels et commerçants français.

Sociality and the rate of molecular evolution

The molecular clock does not tick at a uniform rate in all taxa but maybe influenced by species characteristics. Eusocial species (those with reproductive division of labor) have been predicted to have faster rates of molecular evolution than their nonsocial relatives because of greatly reduced effective population size; if most individuals in a population are nonreproductive and only one or few queens produce all the offspring, then eusocial animals could have much lower effective population sizes than their solitary relatives, which should increase the rate of substitution of nearly neutral mutations. An earlier study reported faster rates in eusocial honeybees and vespid wasps but failed to correct for phylogenetic nonindependence or to distinguish between potential causes of rate variation. Because sociality has evolved independently in many different lineages, it is possible to conduct a more wide-ranging study to test the generality of the relationship. We have conducted a comparative analysis of 25 phylogenetically independent pairs of social lineages and their nonsocial relatives, including bees, wasps, ants, termites, shrimps, and mole rats, using a range of available DNA sequences (mitochondrial and nuclear DNA coding for proteins and RNAs, and nontranslated sequences). By including a wide range of social taxa, we were able to test whether there is a general influence of sociality on rates of molecular evolution and to test specific predictions of the hypothesis: (1) that social species have faster rates because they have reduced effective population sizes; (2) that mitochondrial genes would show a greater effect of sociality than nuclear genes; and (3) that rates of molecular evolution should be correlated with the degree of sociality. We find no consistent pattern in rates of molecular evolution between social and nonsocial lineages and no evidence that mitochondrial genes show faster rates in social taxa. However, we show that the most highly eusocial Hymenoptera do have faster rates than their nonsocial relatives. We also find that social parasites (that utilize the workers from related species to produce their own offspring) have faster rates than their social relatives, which is consistent with an effect of lower effective population size on rate of molecular evolution. Our results illustrate the importance of allowing for phylogenetic nonindependence when conducting investigations of determinants of variation in rate of molecular evolution.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

The multiple pheromone ant clustering algorithm and its application to real world domains

The Multiple Pheromone Ant Clustering Algorithm (MPACA) models the collective behaviour of ants to find clusters in data and to assign objects to the most appropriate class. It is an ant colony optimisation approach that uses pheromones to mark paths linking objects that are similar and potentially members of the same cluster or class. Its novelty is in the way it uses separate pheromones for each descriptive attribute of the object rather than a single pheromone representing the whole object. Ants that encounter other ants frequently enough can combine the attribute values they are detecting, which enables the MPACA to learn influential variable interactions. This paper applies the model to real-world data from two domains. One is logistics, focusing on resource allocation rather than the more traditional vehicle-routing problem. The other is mental-health risk assessment. The task for the MPACA in each domain was to predict class membership where the classes for the logistics domain were the levels of demand on haulage company resources and the mental-health classes were levels of suicide risk. Results on these noisy real-world data were promising, demonstrating the ability of the MPACA to find patterns in the data with accuracy comparable to more traditional linear regression models. © 2013 Polish Information Processing Society.

A multiple pheromone ant clustering algorithm

Ant colony optimisation algorithms model the way ants use pheromones for marking paths to important locations in their environment. Pheromone traces are picked up, followed, and reinforced by other ants but also evaporate over time. Optimal paths attract more pheromone and less useful paths fade away. The main innovation of the proposed Multiple Pheromone Ant Clustering Algorithm (MPACA) is to mark objects using many pheromones, one for each value of each attribute describing the objects in multidimensional space. Every object has one or more ants assigned to each attribute value and the ants then try to find other objects with matching values, depositing pheromone traces that link them. Encounters between ants are used to determine when ants should combine their features to look for conjunctions and whether they should belong to the same colony. This paper explains the algorithm and explores its potential effectiveness for cluster analysis. © 2014 Springer International Publishing Switzerland.

The multiple pheromone Ant clustering algorithm

Ant Colony Optimisation algorithms mimic the way ants use pheromones for marking paths to important locations. Pheromone traces are followed and reinforced by other ants, but also evaporate over time. As a consequence, optimal paths attract more pheromone, whilst the less useful paths fade away. In the Multiple Pheromone Ant Clustering Algorithm (MPACA), ants detect features of objects represented as nodes within graph space. Each node has one or more ants assigned to each feature. Ants attempt to locate nodes with matching feature values, depositing pheromone traces on the way. This use of multiple pheromone values is a key innovation. Ants record other ant encounters, keeping a record of the features and colony membership of ants. The recorded values determine when ants should combine their features to look for conjunctions and whether they should merge into colonies. This ability to detect and deposit pheromone representative of feature combinations, and the resulting colony formation, renders the algorithm a powerful clustering tool. The MPACA operates as follows: (i) initially each node has ants assigned to each feature; (ii) ants roam the graph space searching for nodes with matching features; (iii) when departing matching nodes, ants deposit pheromones to inform other ants that the path goes to a node with the associated feature values; (iv) ant feature encounters are counted each time an ant arrives at a node; (v) if the feature encounters exceed a threshold value, feature combination occurs; (vi) a similar mechanism is used for colony merging. The model varies from traditional ACO in that: (i) a modified pheromone-driven movement mechanism is used; (ii) ants learn feature combinations and deposit multiple pheromone scents accordingly; (iii) ants merge into colonies, the basis of cluster formation. The MPACA is evaluated over synthetic and real-world datasets and its performance compares favourably with alternative approaches.

The relationship between alkaloid-based chemical defenses and diet in dendrobatid poison frogs

Chemical defenses are common among organisms and represent some of the most complex adaptations for avoiding predation, yet our understanding of the ecological nature of these systems remains incomplete. Poison frogs are a group of chemically defended organisms that are dependent entirely on diet for chemical defense. In this study, I identified the dietary arthropods responsible for chemical defense in poison frogs, described spatial and temporal patterns in alkaloid composition of poison frogs, and established links between patterns of variation in alkaloid defense and arthropod diet in poison frogs. Identifying dietary sources and studying patterns of variation in alkaloid composition is fundamental to understanding the ecology and evolution of chemical defense in poison frogs. ^ The dendrobatid poison frog Oophaga pumilio shares many alkaloids in common with other poison frogs and is known to vary in alkaloid composition throughout its geographic range. I designed my dissertation to take advantage of these characteristics and use O. pumilio as a model species for the study of chemical defense in poison frogs. Here, I identified siphonotid millipedes as a source for spiropyrrolizidine alkaloids, formicine ants as a source for pumiliotoxin alkaloids, and oribatid mites as dietary sources for the majority of alkaloids found in poison frogs. I found that alkaloid composition varied spatially and temporally, on both small and large scales, within and among populations of O. pumilio. Alkaloid variation between populations was related to geographic distance, and closer populations tended to have alkaloid compositions more similar to each other than to distant populations. ^ The findings of my study suggest that oribatid mites are the most important dietary source of alkaloids in poison frogs. However, overall alkaloid defense in poison frogs is based on a combination of dietary arthropods, including mites, ants, millipedes, and beetles. Variation in chemical defenses of poison frogs is due to (1) spatial and temporal differences in the presence of alkaloids in certain arthropods and (2) differences in the availability of certain alkaloid-containing arthropods, which are likely the result of differences as well as successional changes in forest structure among locations and through time. ^

Modified continuous ant colony algorithm for function optimization

Many classical as well as modern optimization techniques exist. One such modern method belonging to the field of swarm intelligence is termed ant colony optimization. This relatively new concept in optimization involves the use of artificial ants and is based on real ant behavior inspired by the way ants search for food. In this thesis, a novel ant colony optimization technique for continuous domains was developed. The goal was to provide improvements in computing time and robustness when compared to other optimization algorithms. Optimization function spaces can have extreme topologies and are therefore difficult to optimize. The proposed method effectively searched the domain and solved difficult single-objective optimization problems. The developed algorithm was run for numerous classic test cases for both single and multi-objective problems. The results demonstrate that the method is robust, stable, and that the number of objective function evaluations is comparable to other optimization algorithms.

Decisões comportamentais em contexto de forrageamento de dinoponera quadriceps

The behavioral decisions of animals do not occur randomly, because behaviors are adjusted to ensure the survival and reproduction of the animal. In this research, I examined behavioral decisions in the foraging context of the ant Dinoponera quadriceps with regard to orientation, food avaliation and foraging dynamic to individual level. The study was conducted at the Laboratory of Behavioral Biology at UFRN and in an area of secondary Atlantic Forest in FLONA-ICMBio Nísia Floresta/RN. In all observations and experiments, ants were marked individually with an alphanumeric code label fixed on the thorax. In the first part of the study, I analyzed the orientation cues used by D. quadriceps. The tests were performed in a maze of 17 compartments. Each forager was tested for 10 min in three sessions for six different treatments. The treatments consisted of the presence or absence of odor and superior or frontal visual cues. The workers demonstrated that the presence of odor is indispensable and front visual cues are more effective than superior visual cues. In the second part, I investigated the discrimination of food, considering the parameters, size, weight and volume. In a 'cafeteria' experiment, I offered cylindrical pieces of food (mortadella) in a Petri dish, within an experimental arena 1m². Initially, the pieces were of four different sizes; in a second step, the pieces were of the same size but with different weight; in the last step, the pieces had the same weight but different volumes. The results showed the effect of the size and weight parameters for food choice. In the third part of the study, I evaluated the influence of the activity of active foragers on inactive ones. In this part, the colonies were observed in a natural environment. The observations took place on three consecutive days in 10 episodes, total of 30 days for each colony, 12 hours/day. On the first day, I registered the output and input of workers; on the second day, the most active ants on the first day were taken and given back at the end of the observations; on the third day, the observations were similar to the first day. As a result, the workers of D. quadriceps show autostimulation and they do not show social facilitation and the colony compensates the absence of the most active workers. Based on the stated, I conclude that workers of D. quadriceps use chemical, frontal and superior visual orientation cues during their displacements. They discriminate the chosen food by size and weight. The regulation of activity dynamics of foragers is by autostimulation, an active worker does not influence the activity of an inactive worker, the successful search previous is the stimulus to the successful worker itself to continue foraging activity.

Tomada de decisão individual e aprendizado em Dinoponera quadríceps (Ponerinae, Hymenoptera) forrageando em ambientes dinâmicos

When searching for food, animals often make decisions of where to go, how long to stay in a foraging area and whether or not to return to the last visited spot. These decisions can be enhanced by cognitive traits and adjusted based on previous experience. In social insects such as ants, foraging efficiency have an impact on both individual and colony level. The present study investigated, in the laboratory, the effect of distance from food, capture success and food size, and reward rate on decisions of where to forage in Dinoponera quadriceps, a ponerine ant that forage solitarily and individually make their foraging decisions. We also investigated the influence of learning on the performance of workers over successive trips searching for food by measuring the patch residence time in each foraging trip. Four scenarios were created differing in food reward rates, food size offered and distances colony-food site. Our work has shown that as a rule-of-thumb, workers of D. quadriceps return to the place where a prey item was found on the previous trip, regardless of distance, food size and reward rate. When ants did not capture preys, they were more likely to change path to search for food. However, in one of the scenarios, this decision to switch paths when unsuccessful was less evident, possibly due to the greater variation of possible outcomes ants could experience in this scenario and cognitive constraints of D. quadriceps to predict variations of food distribution. Our results also indicated a learning process of routes of exploration as well as the food site conditions for exploration. After repeated trips, foragers reduced the patch residence time in areas that they did not capture food and quickly changed of foraging area, increasing their foraging efficiency.

Accumulation de dose à partir de champs de déformation 4D appliqués aux traitements au CyberKnife et à l'IMRT

Le cancer pulmonaire est la principale cause de décès parmi tous les cancers au Canada. Le pronostic est généralement faible, de l'ordre de 15% de taux de survie après 5 ans. Les déplacements internes des structures anatomiques apportent une incertitude sur la précision des traitements en radio-oncologie, ce qui diminue leur efficacité. Dans cette optique, certaines techniques comme la radio-chirurgie et la radiothérapie par modulation de l'intensité (IMRT) visent à améliorer les résultats cliniques en ciblant davantage la tumeur. Ceci permet d'augmenter la dose reçue par les tissus cancéreux et de réduire celle administrée aux tissus sains avoisinants. Ce projet vise à mieux évaluer la dose réelle reçue pendant un traitement considérant une anatomie en mouvement. Pour ce faire, des plans de CyberKnife et d'IMRT sont recalculés en utilisant un algorithme Monte Carlo 4D de transport de particules qui permet d'effectuer de l'accumulation de dose dans une géométrie déformable. Un environnement de simulation a été développé afin de modéliser ces deux modalités pour comparer les distributions de doses standard et 4D. Les déformations dans le patient sont obtenues en utilisant un algorithme de recalage déformable d'image (DIR) entre les différentes phases respiratoire générées par le scan CT 4D. Ceci permet de conserver une correspondance de voxels à voxels entre la géométrie de référence et celles déformées. La DIR est calculée en utilisant la suite ANTs («Advanced Normalization Tools») et est basée sur des difféomorphismes. Une version modifiée de DOSXYZnrc de la suite EGSnrc, defDOSXYZnrc, est utilisée pour le transport de particule en 4D. Les résultats sont comparés à une planification standard afin de valider le modèle actuel qui constitue une approximation par rapport à une vraie accumulation de dose en 4D.