An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Training and development in Irish technology companies

The sustained economic growth that has been experienced in the Irish economy in recent years has relied, to a large extent, on the contribution and performance of those industry sectors that possess the ability to provide high-value-added products and services to domestic and international markets. One such contributor has been the Technology sector. However, the performance of this sector relies upon the availability of the necessary capabilities and competencies for Technology companies to remain competitive. The Expert Group on Future Skills Needs have forecasted future skills shortages in this sector. The purpose of this research has been to examine the extent to which Irish Technology companies are taking measures to meet changing skills requirements, through training and development interventions. Survey research methods (in the form of a mail questionnaire, supported by a Web-based questionnaire) have been used to collect information on the expenditure on, and approach to, training and development in these companies, in addition to the methods, techniques and tools/aids that are used to support the delivery of these activities. The contribution of Government intervention has also been examined. The conclusions have been varied. When the activities of the responding companies are considered in isolation, the picture to emerge is primarily positive. Although the expenditure on training and development is slightly lower than that indicated in previous studies, the results vary by company size. Technical employees are clearly the key focus of training provision, while Senior Managers and Directors, Clerical and Administrative staff and Manual workers are a great deal more neglected in training provision. Expenditure on, and use of, computer-based training methods is high, as is the use of most of the specified techniques for facilitating learning. However, when one considers the extent to which external support (in the form of Government interventions and cooperation with other companies and with education and training providers) is integrated into the overall training practices of these companies, significant gaps in practice are identified. The thesis concludes by providing a framework to guide future training and development practices in the Technology sector.

A behavioral study of alpha-1b adrenergic receptor knockout mice: increased reaction to novelty and selectively reduced learning capacities.

Knockout mice lacking the alpha-1b adrenergic receptor were tested in behavioral experiments. Reaction to novelty was first assessed in a simple test in which the time taken by the knockout mice and their littermate controls to enter a second compartment was compared. Then the mice were tested in an open field to which unknown objects were subsequently added. Special novelty was introduced by moving one of the familiar objects to another location in the open field. Spatial behavior and memory were further studied in a homing board test, and in the water maze. The alpha-1b knockout mice showed an enhanced reactivity to new situations. They were faster to enter the new environment, covered longer paths in the open field, and spent more time exploring the new objects. They reacted like controls to modification inducing spatial novelty. In the homing board test, both the knockout mice and the control mice seemed to use a combination of distant visual and proximal olfactory cues, showing place preference only if the two types of cues were redundant. In the water maze the alpha-1b knockout mice were unable to learn the task, which was confirmed in a probe trial without platform. They were perfectly able, however, to escape in a visible platform procedure. These results confirm previous findings showing that the noradrenergic pathway is important for the modulation of behaviors such as reaction to novelty and exploration, and suggest that this is mediated, at least partly, through the alpha-1b adrenergic receptors. The lack of alpha-1b adrenergic receptors in spatial orientation does not seem important in cue-rich tasks but may interfere with orientation in situations providing distant cues only.

Fiscal Policy and Learning

Using the standard real business cycle model with lump-sum taxes, we analyze the impact of fiscal policy when agents form expectations using adaptive learning rather than rational expectations (RE). The output multipliers for government purchases are significantly higher under learning, and fall within empirical bounds reported in the literature (in sharp contrast to the implausibly low values under RE). Effectiveness of fiscal policy is demonstrated during times of economic stress like the recent Great Recession. Finally it is shown how learning can lead to dynamics empirically documented during episodes of 'fiscal consolidations.'

Evolution of decision-making and learning under fluctuating social environments

The capacity to learn to associate sensory perceptions with appropriate motor actions underlies the success of many animal species, from insects to humans. The evolutionary significance of learning has long been a subject of interest for evolutionary biologists who emphasize the bene¬fit yielded by learning under changing environmental conditions, where it is required to flexibly switch from one behavior to another. However, two unsolved questions are particularly impor¬tant for improving our knowledge of the evolutionary advantages provided by learning, and are addressed in the present work. First, because it is possible to learn the wrong behavior when a task is too complex, the learning rules and their underlying psychological characteristics that generate truly adaptive behavior must be identified with greater precision, and must be linked to the specific ecological problems faced by each species. A framework for predicting behavior from the definition of a learning rule is developed here. Learning rules capture cognitive features such as the tendency to explore, or the ability to infer rewards associated to unchosen actions. It is shown that these features interact in a non-intuitive way to generate adaptive behavior in social interactions where individuals affect each other's fitness. Such behavioral predictions are used in an evolutionary model to demonstrate that, surprisingly, simple trial-and-error learn¬ing is not always outcompeted by more computationally demanding inference-based learning, when population members interact in pairwise social interactions. A second question in the evolution of learning is its link with and relative advantage compared to other simpler forms of phenotypic plasticity. After providing a conceptual clarification on the distinction between genetically determined vs. learned responses to environmental stimuli, a new factor in the evo¬lution of learning is proposed: environmental complexity. A simple mathematical model shows that a measure of environmental complexity, the number of possible stimuli in one's environ¬ment, is critical for the evolution of learning. In conclusion, this work opens roads for modeling interactions between evolving species and their environment in order to predict how natural se¬lection shapes animals' cognitive abilities. - La capacité d'apprendre à associer des sensations perceptives à des actions motrices appropriées est sous-jacente au succès évolutif de nombreuses espèces, depuis les insectes jusqu'aux êtres hu¬mains. L'importance évolutive de l'apprentissage est depuis longtemps un sujet d'intérêt pour les biologistes de l'évolution, et ces derniers mettent l'accent sur le bénéfice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est nécessaire de passer de manière flexible d'un comportement à l'autre. Cependant, deux questions non résolues sont importantes afin d'améliorer notre savoir quant aux avantages évolutifs procurés par l'apprentissage. Premièrement, puisqu'il est possible d'apprendre un comportement incorrect quand une tâche est trop complexe, les règles d'apprentissage qui permettent d'atteindre un com¬portement réellement adaptatif doivent être identifiées avec une plus grande précision, et doivent être mises en relation avec les problèmes écologiques spécifiques rencontrés par chaque espèce. Un cadre théorique ayant pour but de prédire le comportement à partir de la définition d'une règle d'apprentissage est développé ici. Il est démontré que les caractéristiques cognitives, telles que la tendance à explorer ou la capacité d'inférer les récompenses liées à des actions non ex¬périmentées, interagissent de manière non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces prédictions comportementales sont utilisées dans un modèle évolutif afin de démontrer que, de manière surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage basé sur l'inférence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxième question quant à l'évolution de l'apprentissage concerne son lien et son avantage relatif vis-à-vis d'autres formes plus simples de plasticité phénotypique. Après avoir clarifié la distinction entre réponses aux stimuli génétiquement déterminées ou apprises, un nouveau fac¬teur favorisant l'évolution de l'apprentissage est proposé : la complexité environnementale. Un modèle mathématique permet de montrer qu'une mesure de la complexité environnementale - le nombre de stimuli rencontrés dans l'environnement - a un rôle fondamental pour l'évolution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant à la mo¬délisation des interactions entre les espèces en évolution et leur environnement, dans le but de comprendre comment la sélection naturelle façonne les capacités cognitives des animaux.

Manipulating sleep spindles - expanding views on sleep, memory, and disease.

Sleep spindles are distinctive electroencephalographic (EEG) oscillations emerging during non-rapid-eye-movement sleep (NREMS) that have been implicated in multiple brain functions, including sleep quality, sensory gating, learning, and memory. Despite considerable knowledge about the mechanisms underlying these neuronal rhythms, their function remains poorly understood and current views are largely based on correlational evidence. Here, we review recent studies in humans and rodents that have begun to broaden our understanding of the role of spindles in the normal and disordered brain. We show that newly identified molecular substrates of spindle oscillations, in combination with evolving technological progress, offer novel targets and tools to selectively manipulate spindles and dissect their role in sleep-dependent processes.

Literacy and Numeracy Learning for Life

Literacy and Numeracy for Learning and Life is the national strategy to improve literacy and numeracy standards among children and young people in the education system. This strategy seeks to address significant concerns about how well our young people are developing the literacy and numeracy skills that they will need to participate fully in the education system, to live satisfying and rewarding lives, and to participate as active and informed citizens in our society.

Literacy and Numeracy Learning for Life - Summary

In July 2011 the Minister for education launched Literacy and Numeracy for Learning and Life – the national strategy to improve literacy and numeracy among children and young people. The strategy was developed following an extensive consultation process and contributions from individuals, schools, groups and organisations. This leaflet gives a flavour of the key parts of the Strategy with access to the full document on the Department’s website.

'Bio-nano interactions: new tools, insights and impacts': summary of the Royal Society discussion meeting

Bio-nano interactions can be defined as the study of interactions between nanoscale entities and biological systems such as, but not limited to, peptides, proteins, lipids, DNA and other biomolecules, cells and cellular receptors and organisms including humans. Studying bio-nano interactions is particularly useful for understanding engineered materials that have at least one dimension in the nanoscale. Such materials may consist of discrete particles or nanostructured surfaces. Much of biology functions at the nanoscale; therefore, our ability to manipulate materials such that they are taken up at the nanoscale, and engage biological machinery in a designed and purposeful manner, opens new vistas for more efficient diagnostics, therapeutics (treatments) and tissue regeneration, so-called nanomedicine. Additionally, this ability of nanomaterials to interact with and be taken up by cells allows nanomaterials to be used as probes and tools to advance our understanding of cellular functioning. Yet, as a new technology, assessment of the safety of nanomaterials, and the applicability of existing regulatory frameworks for nanomaterials must be investigated in parallel with development of novel applications. The Royal Society meeting 'Bio-nano interactions: new tools, insights and impacts' provided an important platform for open dialogue on the current state of knowledge on these issues, bringing together scientists, industry, regulatory and legal experts to concretize existing discourse in science law and policy. This paper summarizes these discussions and the insights that emerged.

The Value of Imperfection : the Wabi-Sabi Principle in Aesthetics and Learning

OER-based learning has the potential to overcome many shortcomings and problems of traditional education. It is not hampered by IP restrictions; can depend on collaborative, cumulative, iterative refinement of resources; and the digital form provides unprecedented flexibility with respect to configuration and delivery. The OER community is a progressive group of educators and learners with decades of learning research to draw from, who know that we must prepare learners for an evolving and diverse reality. Despite this OER tends to replicate the unsuccessful characteristics of traditional education. To remedy this we may need to remember the importance of imperfection, mistakes, problems, disagreement, and the incomplete for engaged learning, and relinquish our notions of perfection, acknowledging that learners learn differently and we need diverse learners. We must stretch our perceptions of quality and provide mechanisms for engaging the incredible pool of educators globally to fulfill the promise of inclusive education.

Transnational study of roles/functions and associated ICT competencies for Higher Education teachers

Aquest estudi forma part del projecte eLene-TLC1 Virtual Campus (2007-2008) recolzat pel programa eLearning de la Comissió Europea. L'objectiu d'aquest projecte és que els professors i els estudiants facin el millor ús possible de les TIC en l'educació superior, preparant als professors per als estudiants de la generació xarxa, permetent als estudiants a la transferència de coneixements i pràctiques de la vida quotidiana per al seu aprenentatge i estimular tant la integració plena de pràctiques innovadores d'ensenyament i d'aprenentatge possibilitades per un entorn tecnològic en constant evolució. Per tal de cobrir part d'aquest objectiu general, es va concebre un estudi per examinar les competències en TIC professors d'Educació Superior en entorns d'aprenentatge en línia.

Using problem-based learning for introducing producer theory and market structure in intermediate microeconomics

This paper shows how instructors can use the problem‐based learning method to introduce producer theory and market structure in intermediate microeconomics courses. The paper proposes a framework where different decision problems are presented to students, who are asked to imagine that they are the managers of a firm who need to solve a problem in a particular business setting. In this setting, the instructors’ role isto provide both guidance to facilitate student learning and content knowledge on a just‐in‐time basis

QuesTInSitu: from tests to routes for assessment in situ activities

Test-based assessment tools are mostly focused on the use of computers. However, advanced Information and Communication Technologies, such as handheld devices, opens up the possibilities of creating new assessment scenarios, increasing the teachers’ choices to design more appropriate tests for their subject areas. In this paper we use the term Computing-Based Testing (CBT) instead of Computer-Based Testing, as it captures better the emerging trends. Within the CBT context, the paper is centred on proposing an approach for “Assessment in situ” activities, where questions have to be answered in front of a real space/location (situ). In particular, we present the QuesTInSitu software implementation that includes both an editor and a player based on the IMS Question and Test Interoperability specification and GoogleMaps. With QuesTInSitu teachers can create geolocated questions and tests (routes), and students can answer the tests using mobile devices with GPS when following a route. Three illustrating scenarios and the results from the implementation of one of them in a real educational situation show that QuesTInSitu enables the creation of innovative, enriched and context-aware assessment activities. The results also indicate that the use of mobile devices and location-based systems in assessment activities facilitates students to put explorative and spatial skills into practice and fosters their motivation, reflection and personal observation.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.