Siliceous spicules and skeleton frameworks in sponges: origin, diversity, ultrastructural patterns, and biological functions

Silica deposition is a fundamental process in sponges. Most sponges in the Classes Demospongiae and Hexactinellida secrete siliceous elements, which can subsequently fuse, interlock with each other, or form three-dimensional structures connected by spongin. The resulting skeletal frameworks allow sponges to grow upwards and facilitate water exchange with minimal metabolic cost. Several studies on sponge skeletogenesis have been published. We are beginning to understand the mechanisms of spicule secretion and the role of spicules and skeletal frameworks in the biology, ecology, and evolution of sponges. Molecular techniques and ecological experiments have demonstrated the genetic control of the process and the contribution of environmental factors to the expression of a sponge spicule, respectively. However, other classic topics such as the role of membranes in silicon transport or whether spicules are formed in situ or secreted anywhere in the sponge mesohyl and then transported to the skeletal framework require further investigation. We review the process of silica deposition in sponges at the molecular and cellular levels, as well as the biological and ecological functions of spicules and skeletons. The genetic control of spicule shapes makes them useful in the reconstruction of sponge phylogeny, although recent experiments have demonstrated the influence of environmental factors in modulating spicule size, shape, and the presence or absence of one or more spicule types. The implications of such variations in sponge taxonomy may be important. Besides supporting sponge cells, spicules can help larvae stay buoyant while in the plankton or reach the bottom at settlement, enhance reproduction success, or catch prey. Conversely, the role of spicules and skeletons in deterring predation has not been demonstrated. Knowledge of several aspects is still based on a single or a few species and extrapolations should be made only with caution. With the advent of new molecular techniques, new lines of research are presently open and active in this field.     

Chemical Defenses of the Sacoglossan Mollusk Elysia rufescens and Its Host Alga Bryopsis sp.

Sacoglossans are a group of opisthobranch mollusks that have been the source of numerous secondary metabolites; however, there are few examples where a defensive ecological role for these compounds has been demonstrated experimentally. We investigated the deterrent properties of the sacoglossan Elysia rufescens and its food alga Bryopsis sp. against natural fish predators. Bryopsis sp. produces kahalalide F, a major depsipeptide that is accumulated by the sacoglossan and that shows in vitro cytotoxicity against several cancer cell lines. Our data show that both Bryopsis sp. and Elysia rufescens are chemically protected against fish predators, as indicated by the deterrent properties of their extracts at naturally occurring concentrations. Following bioassay-guided fractionation, we observed that the antipredatory compounds of Bryopsis sp. were present in the butanol and chloroform fractions, both containing the depsipeptide kahalalide F. Antipredatory compounds of Elysia rufescens were exclusively present in the dichloromethane fraction. Further bioassay-guided fractionation led to the isolation of kahalalide F as the only compound responsible for the deterrent properties of the sacoglossan. Our data show that kahalalide F protects both Bryopsis sp. and Elysia rufescens from fish predation. This is the first report of a diet-derived depsipeptide used as a chemical defense in a sacoglossan.     

Depressed writing: Cognitive distortions in the works of depressed and nondepressed poets and writers.

Previous research has consistently found that poets and writers suffer from higher rates of psychological disturbance than the general population; what is lacking in the research is analysis of the works themselves. This study examined cognitive distortions, a known indicator of depression, in writing samples of 36 eminent depressed authors and 36 eminent nondepressed authors. Results indicate that depressed authors have more cognitive distortions in their writings than nondepressed authors. However, greater significance was found between poets and prose writers, with poets having substantially more cognitive distortions in their works. An interaction effect was found between study group (depressed or control) and medium (poetry or prose) indicating that nondepressed prose writers had significantly fewer cognitive distortions than nondepressed poets, depressed poets, and depressed writers. Differences in gender and time period are explored as well. Findings suggest a general acceptance of depressive thinking and writing for poets that is not present for prose writers. Implications for the effectiveness of writing as a form of psychotherapy are also discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling

Classification with imbalanced data-sets has become one of the most challenging problems in Data Mining. Being one class much more represented than the other produces undesirable effects in both the learning and classification processes, mainly regarding the minority class. Such a problem needs accurate tools to be undertaken; lately, ensembles of classifiers have emerged as a possible solution. Among ensemble proposals, the combination of Bagging and Boosting with preprocessing techniques has proved its ability to enhance the classification of the minority class.In this paper, we develop a new ensemble construction algorithm (EUSBoost) based on RUSBoost, one of the simplest and most accurate ensemble, which combines random undersampling with Boosting algorithm. Our methodology aims to improve the existing proposals enhancing the performance of the base classifiers by the usage of the evolutionary undersampling approach. Besides, we promote diversity favoring the usage of different subsets of majority class instances to train each base classifier. Centered on two-class highly imbalanced problems, we will prove, supported by the proper statistical analysis, that EUSBoost is able to outperform the state-of-the-art methods based on ensembles. We will also analyze its advantages using kappa-error diagrams, which we adapt to the imbalanced scenario.     

Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers

The One-vs-One strategy is one of the most commonly used decomposition technique to overcome multi-class classification problems; this way, multi-class problems are divided into easier-to-solve binary classification problems considering pairs of classes from the original problem, which are then learned by independent base classifiers.The way of performing the division produces the so-called non-competence. This problem occurs whenever an instance is classified, since it is submitted to all the base classifiers although the outputs of some of them are not meaningful (they were not trained using the instances from the class of the instance to be classified). This issue may lead to erroneous classifications, because in spite of their incompetence, all classifiers' decisions are usually considered in the aggregation phase.In this paper, we propose a dynamic classifier selection strategy for One-vs-One scheme that tries to avoid the non-competent classifiers when their output is probably not of interest. We consider the neighborhood of each instance to decide whether a classifier may be competent or not. In order to verify the validity of the proposed method, we will carry out a thorough experimental study considering different base classifiers and comparing our proposal with the best performer state-of-the-art aggregation within each base classifier from the five Machine Learning paradigms selected. The findings drawn from the empirical analysis are supported by the appropriate statistical analysis.     

A biokinematic approach for the computational simulation of proteins molecular mechanism

Proteins play an essential role in the biological processes that take place in the human body. For this reason, it is very important to understand the molecular mechanisms involved. Recently a new approach, based on the parallelism between proteins and spatial mechanisms, has allowed methods to be developed to obtain atomic trajectories for protein motion using concepts from the kinematics of mechanisms. This paper presents a method for simulating protein motion, based on the evaluation of the potential energy during the motion, thereby avoiding the need to perform a minimization procedure. This approach constitutes a good compromise between computational effort and accuracy of the results. In addition, in order to efficiently simulate the motion, a new algorithm for normalization of the structure of the protein is presented. Finally, we illustrate the results of applying this method to an inorganic pyrophosphatase (family II) from Streptococcus gordonii evaluating geometric accuracy, energetic evolution and biological indicators.     

Minimal graphics

Photorealistic rendering of graphical models continues to be a topic of considerable research effort in the computer graphics community. However, photorealism isn't the only criterion for judging an image's value. In visualization, where rendering is the conduit through which abstract, nonrealistic forms (such as isosurfaces or streamtubes) become perceivable to users, photorealism may neither be the most useful nor appropriate property. This article describes our long-term research vision in an area that, in our view, should gain a lot of importance in the future     

Software transactional memories for Scala

Transactional memory is an alternative to locks for handling concurrency in multi-threaded environments. Instead of providing critical regions that only one thread can enter at a time, transactional memory records sufficient information to detect and correct for conflicts if they occur. This paper surveys the range of options for implementing software transactional memory in Scala. Where possible, we provide references to implementations that instantiate each technique. As part of this survey, we document for the first time several techniques developed in the implementation of Manchester University Transactions for Scala. We order the implementation techniques on a scale moving from the least to the most invasive in terms of modifications to the compilation and runtime environment. This shows that, while the less invasive options are easier to implement and more common, they are more verbose and invasive in the codes using them, often requiring changes to the syntax and program structure throughout the code.     

Abstract Interaction Objects

The concept of an ‘interactor’ has been introduced by Faconti and Paterno' [6] as an abstraction of an entity in interactive graphics capable of both input and output. However the notion of interaction object need not be confined to graphics systems; it represents a useful structure for thinking and reasoning about the behaviour of interactive systems in general. As part of Esprit Basic Research Action 7040 (Amodeus-2) we are using the concept of interactor, and existing work on state-based processes and agents, to develop a model and theory of interactive systems. In this paper we describe two formal models for interaction objects and sketch how they can be used to build a small vocabulary of operators to support the rigorous specification of a graphics system. Our model differs from the approach of Faconti and Paterno' in that it abstracts away from any specific graphics framework and is thus suited to the level of abstraction demanded by formal approaches to system development.