Accumulation, Subcellular Distribution and Toxicity of Copper in Earthworm (Eisenia fetida) in the Presence of Ciprofloxacin

Land application of wastes from concentrated animal feeding operations results in accumulation of copper (Cu) and antimicrobials in terrestrial systems. Interaction between Cu and antimicrobials may change Cu speciation in soil solution, and affect Cu bioavailability and toxicity. In this study, earthworms were exposed to quartz sand percolated with different concentrations of Cu and ciprofloxacin (CIP). Copper uptake by earthworms, its subcellular partition, and toxicity were studied. An increase in the applied CIP decreased the free Cu ion concentration in external solution and mortalities of earthworm, while Cu contents in earthworms increased. Copper and CIP in earthworms were fractionated into five fractions: a granular fraction (D), a fraction consisting of tissue fragments, cell membranes, and intact cells (E), a microsomal fraction (F), a denatured proteins fraction (G), and a heat-stable proteins fraction (H). Most of the CIP in earthworms was in fraction H. Copper was redistributed from the metal-sensitive fraction E to fractions D, F, G, and H with increasing CIP concentration. These results challenge the free ion activity model and suggested that Cu may be partly taken up as Cu-CIP complexes in earthworms, changing the bioavailability, subcellular distribution, and toxicity of Cu to earthworms.

Application-Level Energy Awareness for OpenMP

Power, and consequently energy, has recently attained first-class system resource status, on par with conventional metrics such as CPU time. To reduce energy consumption, many hardware- and OS-level solutions have been investigated. However, application-level information - which can provide the system with valuable insights unattainable otherwise - was only considered in a handful of cases. We introduce OpenMPE, an extension to OpenMP designed for power management. OpenMP is the de-facto standard for programming parallel shared memory systems, but does not yet provide any support for power control. Our extension exposes (i) per-region multi-objective optimization hints and (ii) application-level adaptation parameters, in order to create energy-saving opportunities for the whole system stack. We have implemented OpenMPE support in a compiler and runtime system, and empirically evaluated its performance on two architectures, mobile and desktop. Our results demonstrate the effectiveness of OpenMPE with geometric mean energy savings across 9 use cases of 15 % while maintaining full quality of service.

Dynamic Network Mechanisms of Relational Integration

A prominent hypothesis states that specialized neural modules within the human lateral frontopolar cortices (LFPCs) support “relational integration” (RI), the solving of complex problems using inter-related rules. However, it has been proposed that LFPC activity during RI could reflect the recruitment of additional “domain-general” resources when processing more difficult problems in general as opposed to RI specifi- cally. Moreover, theoretical research with computational models has demonstrated that RI may be supported by dynamic processes that occur throughout distributed networks of brain regions as opposed to within a discrete computational module. Here, we present fMRI findings from a novel deductive reasoning paradigm that controls for general difficulty while manipulating RI demands. In accordance with the domain- general perspective, we observe an increase in frontoparietal activation during challenging problems in general as opposed to RI specifically. Nonetheless, when examining frontoparietal activity using analyses of phase synchrony and psychophysiological interactions, we observe increased network connectivity during RI alone. Moreover, dynamic causal modeling with Bayesian model selection identifies the LFPC as the effective connectivity source. Based on these results, we propose that during RI an increase in network connectivity and a decrease in network metastability allows rules that are coded throughout working memory systems to be dynamically bound. This change in connectivity state is top-down propagated via a hierarchical system of domain-general networks with the LFPC at the apex. In this manner, the functional network perspective reconciles key propositions of the globalist, modular, and computational accounts of RI within a single unified framework.

Exploration du lien entre la qualité de la mentalisation et l'efficacité du rappel autobiographique

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Alteración de la atención y concentración en estudiantes de medicina que realizan turnos de noche

El sueño, es indispensable para la recuperación, física, mental y de procesos como la consolidación de memoria, atención y lenguaje. La privación de sueño (PS) incide en la atención y concentración. La PS es inherente a la formación médica, pero no es claro el papel de los turnos nocturnos en estudiantes, porque no cumplen con un objetivo académico, pero hay relación con disminución de la salud, productividad, accidentes, y alteraciones en diversas actividades. Está descrito el impacto de la PS sobre la capacidad de aprendizaje y aspectos como el ánimo y las relaciones interpersonales. MÉTODOS: Se realizó un estudio analítico observacional de cohorte longitudinal, con tres etapas de medición a 180 estudiantes de Medicina de la Universidad del Rosario, que evaluó atención selectiva y concentración mediante la aplicación de la prueba d2, validada internacionalmente para tal fin. RESULTADOS: Se estudiaron 180 estudiantes, 115 mujeres, 65 hombres, entre los 18 y 26 años (promedio 21). Al inicio del estudio dormían en promedio 7,9 horas, cifra que se redujo a 5,8 y 6,3 en la segunda y tercera etapa respectivamente. El promedio de horas de sueño nocturno, disminuyó en el segundo y tercer momento (p<0,001); Además se encontró mediante la aplicación de la prueba d2, que hubo correlación significativa directa débil, entre el promedio de horas de sueño, y el promedio del desempeño en la prueba (r=0.168, p=0.029) CONCLUSIONES: La PS, con períodos de sueño menores a 7,2 horas, impactan de manera importante la atención selectiva, la concentración

Efectividad del tratamiento con neurofeedback en trastornos del estado del ánimo, ansiedad y fibromialgia: una revisión de literatura

El neurofeedback es una técnica no invasiva en la que se pretende corregir, mediante condicionamiento operante, ondas cerebrales que se encuentren alteradas en el electroencefalograma. Desde 1967, se han conducido numerosas investigaciones relacionadas con los efectos de la técnica en el tratamiento de alteraciones psicológicas. Sin embargo, a la fecha no existen revisiones sistemáticas que reúnan los temas que serán aquí tratados. El aporte de este trabajo es la revisión de 56 artículos, publicados entre los años 1995 y 2013 y la evaluación metodológica de 29 estudios incluidos en la revisión. La búsqueda fue acotada a la efectividad del neurofeedback en el tratamiento de depresión, ansiedad, trastorno obsesivo compulsivo (TOC), ira y fibromialgia. Los hallazgos demuestran que el neurofeedback ha tenido resultados positivos en el tratamiento de estos trastornos, sin embargo, es una técnica que aún está en desarrollo, con unas bases teóricas no muy bien establecidas y cuyos resultados necesitan de diseños metodológicamente más sólidos que ratifiquen su validez.

Examen neurológico

Se realizó un capítulo sobre la descripción del examen neurológico como herramienta principal en el abordaje del paciente con patología neurológica.

Dynamic load balancing in parallel KD-tree k-means

One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing issue. Three solutions have been developed and tested. Two approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy.

Norms-based simulation for personalised service provision

User interfaces have the primary role of enabling access to information meeting individual users' needs. However, the user-systems interaction is still rigid, especially in support of complex environments where various types of users are involved. Among the approaches for improving user interface agility, we present a normative approach to the design interfaces of web applications, which allow delivering users personalized services according to parameters extracted from the simulation of norms in the social context. A case study in an e-Government context is used to illustrate the implications of the approach.

Epidemic K-Means clustering

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. This work proposes a fully decentralised algorithm (Epidemic K-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art distributed K-Means algorithms based on sampling methods. The experimental analysis confirms that the proposed algorithm is a practical and accurate distributed K-Means implementation for networked systems of very large and extreme scale.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

L3 acquisition: a focus on cognitive approaches

Interest in third language (L3) acquisition has increased exponentially in recent years, due to its potential to inform long-lasting debates in theoretical linguistics, language acquisition and psycholinguistics. Researchers investigating child and adult L3 acquisition have, from the very beginning, considered the many different cognitive factors that constrain and condition the initial state and development of newly acquired languages, and their models have duly evolved to incorporate insights from the most recent findings in psycholinguistics, neurolinguistics and cognitive psychology. The articles in this Special Issue of Bilingualism: Language and Cognition, in dealing with issues such as age of acquisition, attrition, relearning, cognitive economy or the reliance on different memory systems –to name a few–, provide an accurate portrayal of current inquiry in the field, and are a particularly fine example of how instrumental research in language acquisition and other cognitive domains can be to one another.

O comércio eletrônico e sua aplicação no planejamento e gestão do processo de compras da cadeia de suprimentos: um estudo na indústria química

Após endereçar questões relativas à integração de suas funções internas, a empresa está se voltando para a criação de um ambiente externo interconectado com seus parceiros de negócio. Torna-se imperativo o aperfeiçoamento do relacionamento com estes parceiros, por meio de processos automatizados, que gerenciam as Cadeias de Suprimento e Distribuição formadas. A Tecnologia de Informação, ao permitir a conexão de processos e a interoperação de sistemas, passa a ser um instrumento essencial nesta transformação. Especificamente, o ambiente digital de negócio construído sobre a Infovia, formada principalmente pela Internet e seus serviços, como a WWW, confere avanços consideráveis ao relacionamento dentro da organização, entre organizações e entre estas e seus clientes. A esta transformação do negócio, baseada na Tecnologia de Informação, conectado às redes de comunicação, chama-se Comércio Eletrônico. Com base no conhecimento consolidado pelo referencial teórico disponível e nos resultados obtidos com o estudo de caso conduzido, o autor identifica e categoriza as influências do Comércio Eletrônico nos processos de compra da Cadeia de Suprimentos na indústria química, estruturando estas influências em relação aos modelos de negócios digitais conhecidos.

Investigação do funcionamento cognitivo de pacientes pediátricos diagnosticados com leucemia linfóide aguda - LLA

The present work investigated the cognitive operation of children diagnosed with acute lymphoblastic leukaemia (ALL), accompanied at pediatric oncologic institutions at the city of Natal/RN. Had participated in this study twenty children, of both sexes, between six and twelve years old, with the ALL diagnostic, who were in treatment (n=10) and out of treatment for at least one year (n=10) and were submitted exclusively to chemotherapy as CNS prophylaxis. The utilized protocol of neuropsychological evaluation covered the following cognitive abilities: intellective capability, attentional and memory systems, and executive functions. Data was analyzed through descriptive and inferential measures, with the support of the Mann-Whitney U Test and T-test, considering the influence of the variables sex, age at diagnostic and the past time since the beginning of the treatment over children s performance. The intellective capability evaluation showed low score to the out-of-treatment groups, female and children under five years old to the diagnostic. In concern of attentional systems, groups showed the expected performance. In a relevant way, in the evaluation of executive functions, were found reduced scores within all groups, especially inside the in-treatment group. Memory evaluation pointed to reduced performance in items concerning to learning evolution and spontaneous evocation after interference to the several groups. It can be concluded, reffer to the occurrence of transitory and permanent impact associated to the intrusion of chemotherapic components during the maturational course of the CNS. It s expected that the present investigation and the development of similar studies enable major comprehension about the mode, extension and repercussion of these damages subsidizing the development of strategies which may minimize them and provide better xxiii life quality to this clinical subgroup