The writings of Mark Twain [pseud.]

Each vol. has general half-title and special t.-p.

The complete works of Mark Twain.

v.1. The adventures of Tom Sawyer -- v.2. The innocents abroad -- v.3. Pudd'nhead Wilson -- v.4. The America claimant -- v.5. A Connecticut Yankee in King Arthur's court -- v.6. Roughing it -- v.7. Life on the Mississippi -- v.8. The mysterious stranger -- v.9. The adventures of Huckleberry Finn -- v.10. The gilded age -- v.11. A tramp abroad -- v.12. What is man? -- v.13. Following the equator -- v.14. Tom Sawyer abroad -- v.15. The man that corrupted Hadleyburg -- v.16. In defense of Harriet Shelley -- v.17. Joan of Arc -- v.18. The $30,000 bequest -- v.19. Sketches old and new -- v.20. Europe and elsewhere -- v.21. The prince and the pauper -- v.22. Mark Twain's notebook -- v.23. Christian science -- v.24. Mark Twain's speeches.

The writings of Mark Twain.

URL additional copies of v.6 and 19: Hillcrest Edition.

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.

The adventures of two Alabama boys /

pt.l. The adventures of Dr. H. J. Crumpton, in his efforts to reach the gold fields of 1849.-pt.2. The adventures of W. B. Crumpton, going to and returning from California, including his lecture, "The original tramp, or How a boy got through the lines to the Confederacy".-pt.3. To California and back after a lapse of forty years, by W. B. Crumpton.

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.

The complete works of Mark Twain

- Autobiography. - [v.1.] The adventures of Tom Sawyer. -[v.2.] The innocents abroad. - [v.3.] Pudd'nhead Wilson. - [v.4.] The American claimant. - [v.5.] Connecticut Yankee in King Arthur's court. -[v.6.] Roughing it. - [v.7.] Life on the Mississippi. - [v.8.] The mysterious stranger. -[v.9.] The adventures of Huckleberry Finn. - [v.10.] The gilded age. - [v.11.] A tramp abroad. -[v.12.] What is man? - [v.13.] Following the equator. -[v.14.] Tom Sawyer abroad. - [v.15.] The man that corrupted Hadleybrug. -[v.16.] In defense of Harriet Shelley. - [v.17.] Joan of Arc. - [v.18.] The 30,000 bequest. - [v.19.] Sketches, new and old. - [v.20.] Europe and elsewhere. - [v.21.] - The Prince and the pauper. - [v.22.] Mark Twain's notebook. - [v.23.] Christian Science. -[v.24.] Mark Twain's speeches.

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. ^